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About me

Shaping the Future of Mobility with Human-Centered AI

For over 15 years, I have turned idea concepts into production-ready intelligent systems at Intel and BMW. As a research professor, I am now pioneering the next wave of human-centric AI to build a safer, more intelligent mobility future. This site is an exploration of that journey at the intersection of AI, automotive, and user experience.

Prof. Dr. Alvarez

Archive Layout with Content

A variety of common markup showing how the theme styles them.

Header one

Header two

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Header six

Blockquotes

Single line blockquote:

Quotes are cool.

Tables

Entry	Item
John Doe	2016	Description of the item in the list
Jane Doe	2019	Description of the item in the list
Doe Doe	2022	Description of the item in the list

Header1	Header2	Header3
cell1	cell2	cell3
cell4	cell5	cell6
cell1	cell2	cell3
cell4	cell5	cell6
Foot1	Foot2	Foot3

Definition Lists

Definition List Title: Definition list division.
Startup: A startup company or startup is a company or temporary organization designed to search for a repeatable and scalable business model.
#dowork: Coined by Rob Dyrdek and his personal body guard Christopher “Big Black” Boykins, “Do Work” works as a self motivator, to motivating your friends.
Do It Live: I’ll let Bill O’Reilly explain this one.

Unordered Lists (Nested)

List item one
- List item one
  - List item one
  - List item two
  - List item three
  - List item four
- List item two
- List item three
- List item four
List item two
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List item four

Ordered List (Nested)

List item one
1. List item one
  1. List item one
  2. List item two
  3. List item three
  4. List item four
2. List item two
3. List item three
4. List item four
List item two
List item three
List item four

Buttons

Make any link standout more when applying the .btn class.

Notices

Watch out! You can also add notices by appending {: .notice} to a paragraph.

HTML Tags

Address Tag

1 Infinite Loop
Cupertino, CA 95014
United States

Anchor Tag (aka. Link)

This is an example of a link.

Abbreviation Tag

The abbreviation CSS stands for “Cascading Style Sheets”.

Cite Tag

“Code is poetry.” —Automattic

Code Tag

You will learn later on in these tests that word-wrap: break-word; will be your best friend.

Strike Tag

This tag will let you ~~strikeout text~~.

Emphasize Tag

The emphasize tag should italicize text.

Insert Tag

This tag should denote inserted text.

Keyboard Tag

This scarcely known tag emulates keyboard text, which is usually styled like the <code> tag.

Preformatted Tag

This tag styles large blocks of code.

.post-title {
  margin: 0 0 5px;
  font-weight: bold;
  font-size: 38px;
  line-height: 1.2;
  and here's a line of some really, really, really, really long text, just to see how the PRE tag handles it and to find out how it overflows;
}

Quote Tag

Developers, developers, developers… –Steve Ballmer

Strong Tag

This tag shows bold text.

Subscript Tag

Getting our science styling on with H₂O, which should push the “2” down.

Superscript Tag

Still sticking with science and Isaac Newton’s E = MC², which should lift the 2 up.

Variable Tag

This allows you to denote variables.

Page Not Found

Sorry, but the page you were trying to view does not exist — perhaps you can try searching for it below.

About me

Shaping the Future of Mobility with Human-Centered AI

For over 15 years, I have turned idea concepts into production-ready intelligent systems at Intel and BMW. As a research professor, I am now pioneering the next wave of human-centric AI to build a safer, more intelligent mobility future. This site is an exploration of that journey at the intersection of AI, automotive, and user experience.

Prof. Dr. Alvarez

Archive Layout with Content

A variety of common markup showing how the theme styles them. # Header one ## Header two ### Header three #### Header four ##### Header five ###### Header six ## Blockquotes Single line blockquote: > Quotes are cool. ## Tables | Entry | Item | | | -------- | ------ | ------------------------------------------------------------ | | [John Doe](#) | 2016 | Description of the item in the list | | [Jane Doe](#) | 2019 | Description of the item in the list | | [Doe Doe](#) | 2022 | Description of the item in the list | | Header1 | Header2 | Header3 | |:--------|:-------:|--------:| | cell1 | cell2 | cell3 | | cell4 | cell5 | cell6 | |-----------------------------| | cell1 | cell2 | cell3 | | cell4 | cell5 | cell6 | |=============================| | Foot1 | Foot2 | Foot3 | ## Definition Lists Definition List Title : Definition list division. Startup : A startup company or startup is a company or temporary organization designed to search for a repeatable and scalable business model. #dowork : Coined by Rob Dyrdek and his personal body guard Christopher "Big Black" Boykins, "Do Work" works as a self motivator, to motivating your friends. Do It Live : I'll let Bill O'Reilly [explain](https://www.youtube.com/watch?v=O_HyZ5aW76c "We'll Do It Live") this one. ## Unordered Lists (Nested) * List item one * List item one * List item one * List item two * List item three * List item four * List item two * List item three * List item four * List item two * List item three * List item four ## Ordered List (Nested) 1. List item one 1. List item one 1. List item one 2. List item two 3. List item three 4. List item four 2. List item two 3. List item three 4. List item four 2. List item two 3. List item three 4. List item four ## Buttons Make any link standout more when applying the `.btn` class. ## Notices **Watch out!** You can also add notices by appending `{: .notice}` to a paragraph. {: .notice} ## HTML Tags ### Address Tag

1 Infinite Loop
Cupertino, CA 95014
United States

### Anchor Tag (aka. Link) This is an example of a [link](http://github.com "Github"). ### Abbreviation Tag The abbreviation CSS stands for "Cascading Style Sheets". *[CSS]: Cascading Style Sheets ### Cite Tag "Code is poetry." ---Automattic ### Code Tag You will learn later on in these tests that `word-wrap: break-word;` will be your best friend. ### Strike Tag This tag will let you ~~strikeout text~~. ### Emphasize Tag The emphasize tag should _italicize_ text. ### Insert Tag This tag should denote inserted text. ### Keyboard Tag This scarcely known tag emulates keyboard text, which is usually styled like the `

` tag. ### Preformatted Tag This tag styles large blocks of code..post-title {
  margin: 0 0 5px;
  font-weight: bold;
  font-size: 38px;
  line-height: 1.2;
  and here's a line of some really, really, really, really long text, just to see how the PRE tag handles it and to find out how it overflows;
}
### Quote Tag Developers, developers, developers… –Steve Ballmer ### Strong Tag This tag shows **bold text**. ### Subscript Tag Getting our science styling on with H₂O, which should push the "2" down. ### Superscript Tag Still sticking with science and Isaac Newton's E = MC², which should lift the 2 up. ### Variable Tag This allows you to denote variables. {% include base_path %} {% for post in site.pages %} {% include archive-single.html %} {% endfor %} </article> </div> Posts by Category
{% include base_path %} {% include group-by-array collection=site.posts field="categories" %} {% for category in group_names %} {% assign posts = group_items[forloop.index0] %}{{ category }}
{% for post in posts %} {% include archive-single.html %} {% endfor %} {% endfor %}
 Posts by Collection
{% include base_path %} {% capture written_label %}'None'{% endcapture %} {% for collection in site.collections %} {% unless collection.output == false or collection.label == "posts" %} {% capture label %}{{ collection.label }}{% endcapture %} {% if label != written_label %}{{ label }}
{% capture written_label %}{{ label }}{% endcapture %} {% endif %} {% endunless %} {% for post in collection.docs %} {% unless collection.output == false or collection.label == "posts" %} {% include archive-single.html %} {% endunless %} {% endfor %} {% endfor %}
 CV
{% include base_path %} Education ====== * 2012 International Ph.D in Computer Science, University of the Basque Country & Clemson University * Supervisors: Karmele Lopez Ipina & Juan E. Gilbert * Graduated Summa Cum Laude * 2011 Advance Graduate Research in Engineering, University of Burgos * Graduated Summa Cum Laude * 2006 B.S. in Communication Sciences, University of Burgos * Graduated Summa Cum Laude Work experience ====== * 01/2025 - Present: Profesor of Human-centered Intelligent Systems * THI, Computer Science Faculty, AI Motion Institute * 12/2021 - Present: Principal Engineer, Technical Assistant to Intel Labs Director * Intel Corporation, Intel Labs, Autonomous Driving Research * 09/2014 - 12/2021: Sr. Research Scientist * Intel Corporation, Intel Labs, Autonomous Driving Research * 11/2013 - 09/2014 : Sr. Manager, IT Projects * BMW China * 12/2012 - 11/2013 : Manager, Connected Drive Asia Pacific * BMW China * 02/2012 - 12/2012 : IT Architect * BMW AG * 09/2009 - 02/2012 : Research Associate * BMW USA, Greenville, SC * 09/2009 - 02/2012 : Graudate Research Assistant * Clemson University, School of Computing Skills ====== * Research * Automotive * Automated Driving * Advance Driving Assistance Systems * Safety * Perception * Planning * Connected Vehicles - V2X * Human Factors * Human-Machine Interactions * Driving Monitoring Systems * Technical * Programming * Data Analytics * Product Development * Prototyping * Problem Solving * Languages * Spanish - Native * English / German - Fluent * Chinese - Basic Publications ======{% for post in site.publications %} {% include archive-single-cv.html %} {% endfor %}
Talks ======{% for post in site.talks %} {% include archive-single-talk-cv.html %} {% endfor %}
Teaching ======{% for post in site.teaching %} {% include archive-single-cv.html %} {% endfor %}
Service and leadership ====== * IEEE Sr. Member * ACM Member * IJHCI - Board Member * AutoUI - SC Member * IEEE ITSS - Member
 
/* * Minimal Mistakes Jekyll Theme * * - Michael Rose * - mademistakes.com * - https://twitter.com/mmistakes * */ @import "vendor/breakpoint/breakpoint"; // media query mixins @import "variables"; @import "mixins"; @import "vendor/susy/susy"; @import "reset"; @import "base"; @import "utilities"; @import "animations"; @import "tables"; @import "buttons"; @import "citations"; @import "notices"; @import "masthead"; @import "navigation"; @import "footer"; @import "syntax"; @import "forms"; @import "page"; @import "archive"; @import "sidebar"; @import "vendor/font-awesome/fontawesome"; @import "vendor/font-awesome/solid"; @import "vendor/font-awesome/brands"; @import "vendor/magnific-popup/magnific-popup"; @import "print"; @import "dark-theme";
 Markdown
## Locations of key files/directories * Basic config options: _config.yml * Top navigation bar config: _data/navigation.yml * Single pages: _pages/ * Collections of pages are .md or .html files in: * _publications/ * _portfolio/ * _posts/ * _teaching/ * _talks/ * Footer: _includes/footer.html * Static files (like PDFs): /files/ * Profile image (can set in _config.yml): images/profile.png ## Tips and hints * Name a file ".md" to have it render in markdown, name it ".html" to render in HTML. * Go to the [commit list](https://github.com/academicpages/academicpages.github.io/commits/master) (on your repo) to find the last version Github built with Jekyll. * Green check: successful build * Orange circle: building * Red X: error * No icon: not built ## Resources * [Liquid syntax guide](https://shopify.github.io/liquid/tags/control-flow/) ## Markdown guide ### Header three #### Header four ##### Header five ###### Header six ## Blockquotes Single line blockquote: > Quotes are cool. ## Tables ### Table 1 | Entry | Item | | | -------- | ------ | ------------------------------------------------------------ | | [John Doe](#) | 2016 | Description of the item in the list | | [Jane Doe](#) | 2019 | Description of the item in the list | | [Doe Doe](#) | 2022 | Description of the item in the list | ### Table 2 | Header1 | Header2 | Header3 | |:--------|:-------:|--------:| | cell1 | cell2 | cell3 | | cell4 | cell5 | cell6 | |-----------------------------| | cell1 | cell2 | cell3 | | cell4 | cell5 | cell6 | |=============================| | Foot1 | Foot2 | Foot3 | ## Definition Lists Definition List Title : Definition list division. Startup : A startup company or startup is a company or temporary organization designed to search for a repeatable and scalable business model. #dowork : Coined by Rob Dyrdek and his personal body guard Christopher "Big Black" Boykins, "Do Work" works as a self motivator, to motivating your friends. Do It Live : I'll let Bill O'Reilly [explain](https://www.youtube.com/watch?v=O_HyZ5aW76c "We'll Do It Live") this one. ## Unordered Lists (Nested) * List item one * List item one * List item one * List item two * List item three * List item four * List item two * List item three * List item four * List item two * List item three * List item four ## Ordered List (Nested) 1. List item one 1. List item one 1. List item one 2. List item two 3. List item three 4. List item four 2. List item two 3. List item three 4. List item four 2. List item two 3. List item three 4. List item four ## Buttons Make any link standout more when applying the `.btn` class. ## Notices **Watch out!** You can also add notices by appending `{: .notice}` to a paragraph. {: .notice} ## HTML Tags ### Address Tag 1 Infinite Loop
 Cupertino, CA 95014
 United States
### Anchor Tag (aka. Link) This is an example of a [link](http://github.com "Github"). ### Abbreviation Tag The abbreviation CSS stands for "Cascading Style Sheets". *[CSS]: Cascading Style Sheets ### Cite Tag "Code is poetry." ---Automattic ### Code Tag You will learn later on in these tests that `word-wrap: break-word;` will be your best friend. ### Strike Tag This tag will let you strikeout text. ### Emphasize Tag The emphasize tag should _italicize_ text. ### Insert Tag This tag should denote inserted text. ### Keyboard Tag This scarcely known tag emulates keyboard text, which is usually styled like the `` tag. ### Preformatted Tag This tag styles large blocks of code..post-title {
  margin: 0 0 5px;
  font-weight: bold;
  font-size: 38px;
  line-height: 1.2;
  and here's a line of some really, really, really, really long text, just to see how the PRE tag handles it and to find out how it overflows;
}
### Quote Tag Developers, developers, developers… –Steve Ballmer ### Strong Tag This tag shows **bold text**. ### Subscript Tag Getting our science styling on with H₂O, which should push the "2" down. ### Superscript Tag Still sticking with science and Isaac Newton's E = MC², which should lift the 2 up. ### Variable Tag This allows you to denote variables. </article> </div> Page not in menu
This is a page not in the menu. You can use markdown in this page. Heading 1 ====== Heading 2 ======
 Page Archive
{% include base_path %} {% for post in site.pages %} {% include archive-single.html %} {% endfor %}
 Patents
 {% include base_path %} {% assign postsByYear = site.patents | group_by_exp:"post", "post.date | date: '%Y'" | reverse %} {% for year_group in postsByYear %} <details class="year-details" {% if forloop.first %}open{% endif %}> {{ year_group.name }}
 {%- comment -%} Using 'publications-list-indent' for consistency, can be renamed {%- endcomment -%} {% for post in year_group.items reversed %} {%- comment -%} Using 'publication-item' for consistency, can be renamed {%- endcomment -%} {% include archive-single.html %}
{% endfor %}
</details> {% endfor %}
 Portfolio
{% include base_path %} {% for post in site.portfolio %} {% include archive-single.html %} {% endfor %}
 Publications
{% if author.googlescholar %} You can also find my articles on my Google Scholar profile. {% endif %}  {% include base_path %} {% assign postsByYear = site.publications | group_by_exp:"post", "post.date | date: '%Y'" | reverse %} {% for year_group in postsByYear %} <details class="year-details" {% if forloop.first %}open{% endif %}> {{ year_group.name }}
 {% for post in year_group.items reversed %} {% include archive-single.html %}
{% endfor %}
</details> {% endfor %}
 Executive Portfolio
 IGNACIO ALVAREZ
Technology Executive | Automotive & AI Leader
📍 Based in Germany · Open to Relocation Globally
   ignacio.alvarez@thi.de    LinkedIn 
   Download PDF Resume    Listen Resume Podcast 
 
Executive Summary
 Technology Executive with a track record of CTO-level impact in the Automotive & AI sectors. Proven leader with 15+ years of experience architecting and scaling intelligent systems from R&D to series production, driving 30% YoY R&D ROI amidst budget reductions, leading global R&D organizations of 100+ engineers, and launching platforms adopted by 1M+ users. Expert in defining technical vision that aligns with C-suite objectives, secures market leadership, and generates massive IP value (150+ patents).
Core Competencies
 Global R&D Leadership (100+ engineers) Technology Strategy & Roadmaps Productization & Tech Transfer Automated Driving Architecture Compute Platform Strategy Artificial Intelligence & Machine Learning Deep Learning & Generative AI Functional Safety Connected Vehicles (V2X) User-Centered Design Human–Machine Interface (HMI) Cyber-Physical Security Digital Twins & Large Scale Simulation Edge AI & Perception Stacks Behavior/Planning Verification & Validation Connected Vehicle Services Standards Leadership & Ecosystem Patent Strategy & Portfolio Development Cross-Functional Leadership Fluent in English, Spanish & German Basic Mandarin
Professional Experience
 Tenured Research Professor of Human-centered Intelligent Systems
Technische Hochschule Ingolstadt
2025–Present
 
  Tenured research professor at AI Motion Bavaria Research Institute. Leading European Research Initiatives in AI and Automotive. Sponsored by the High-Tech Agenda Program of the Government of Bavaria with a €3M grant to develop a research program in human-centered intelligent vehicles.
Challenge:
Establish and lead cutting-edge research initiatives in human-centered intelligent vehicles while securing substantial funding and building academic-industry partnerships.
Action:
Secured €3M grant from the High-Tech Agenda Program of the Government of Bavaria to develop comprehensive research programs in AI and Automotive. Established THI Doctoral School membership and developed advanced curriculum for Automotive AI Masters and User Experience Design programs.
Result:
Successfully positioned THI as a leading European research institution in AI and Automotive, with substantial funding secured and advanced educational programs established across Computer Science and Engineering faculties.
Principal Engineer & Executive Technical Advisor
Intel Corporation – Intel Labs
2021–2025
 
  Reporting to the Intel Labs Director. Tasked with maximizing R&D value amidst an average 8% yearly budget reduction. Advised C-suite on strategic technology investments, accelerating product impact and optimizing IP utilization. Drove an average 30% YoY increase in R&D ROI, reversing the impact of budget cuts.
Challenge:
Maximize R&D value and strategic impact while managing 8% yearly budget reductions across Intel Labs' global engineering organization, requiring innovative approaches to resource optimization and strategic technology investments.
Action:
Established unified research strategies and cross-functional collaboration across Intel Labs' 500+ person global engineering organization. Steered autonomous driving R&D strategy aligning 100+ multi-regional engineering resources. Served as key technology advisor for M&A, divestiture, and IPO activities within the automotive sector.
Result:
Achieved average 30% YoY increase in R&D ROI, reversing the impact of budget cuts across semiconductor design, packaging, systems, networking, software, algorithmic development and AI. Successfully architected and deployed production-grade automated driving safety systems, reducing validation cycles by ~20% and accelerating compliance with emerging safety regulations.
Senior Research Scientist & Autonomous Driving Lead
Intel Corporation – Intel Labs
2014–2021
 
  Directed development of a multimodal in-vehicle R&D platform ("Skyline") integrating driver monitoring, voice assistants, and AR displays. Supporting design winds of Intel Automotive technology by 10+ global automotive OEMs and showcased the platform at international industry forums.
Challenge:
Develop and commercialize a comprehensive multimodal in-vehicle R&D platform that integrates cutting-edge technologies while establishing Intel as a leading automotive technology partner and securing substantial research funding.
Action:
Led cross-institutional research collaborations, securing $5M+ in funding and partnerships. Transferred multiple key technologies to Intel's and Mobileye product teams. Led open-source contributions to industry bodies and international standards, bridging research to real-world impact.
Result:
Successfully deployed "Skyline" platform across 10+ global automotive OEMs, showcased at international forums (Google IO, Intel 360). Secured 50+ granted patents and 100+ pending in autonomous systems, AI and robotics. Published 70+ papers and built thought leadership in automotive AI safety and HMI.
Senior IT Manager & Connected Drive Lead
BMW Group
2012–2014
 
  Led a 100+ IT engineering division across Asia-Pacific, delivering next-generation connected vehicle services to drive rapid expansion into the region's fastest-growing market. Oversaw data centers, telematics, and in-car digital services for BMW and MINI brands in China and Asia Pacific markets.
Challenge:
Lead rapid expansion into Asia-Pacific's fastest-growing automotive market while ensuring 24/7 reliability and scalability of connected vehicle services during record growth, and differentiate BMW in a competitive luxury market through innovative in-car technology.
Action:
Managed 100+ IT engineering division across Asia-Pacific, overseeing data centers, telematics, and in-car digital services. Pioneered in-car voice and AI assistants, collaborating with partners like Nuance and Microsoft. Developed integrated multimodal user manual for BMW 7 series.
Result:
Achieved over 1 million new users in the first year, significantly enhancing customer engagement. Launched industry's first natural-language voice assistant and multimodal user manual, setting new standards for in-vehicle HMI and earning industry-wide commendations for innovation.
Advanced Research Roles
BMW IT Research Center and Clemson University, I-CAR
2009–2012
 
  Held advanced research roles with BMW Technology Office USA and Clemson University, developing functional prototypes for commercial systems. Contributed to the first proofs-of-concept for location-based services, voice-activated navigation, augmented reality user manuals, and driver assistance prototypes.
Challenge:
Develop cutting-edge automotive technology prototypes that can inform future product roadmaps while bridging academic research with commercial applications in the automotive industry.
Action:
Collaborated between BMW Technology Office USA and Clemson University to develop functional prototypes for location-based services, voice-activated navigation, augmented reality user manuals, and driver assistance systems.
Result:
Successfully created first proofs-of-concept that informed future BMW product roadmaps, establishing foundational technologies for connected vehicle services and advanced driver assistance systems.
Professional Leadership & Affiliations
  IEEE Intelligent Transportation Systems Society – Member of Board of Governors (elected role driving global ITS strategy)
  ACM AutomotiveUI Conference – Steering Committee Member (shaping annual agenda for automotive UX innovation)
  Editorial Boards – Board Member for IEEE ITS Magazine and Int. Journal of Human-Computer Interaction
  Senior Member, IEEE and Member, ACM, SAE (recognized professional standing and contributions in tech)
Thought Leadership & Innovation
 Patents
Holder of 150+ patents (50+ granted) in autonomous systems, AI, and robotics, shaping the core IP strategy for market-leading mobility solutions.
View Patent Portfolio 
 Publications
Author of 70+ publications in top-tier IEEE and ACM venues and editor of multiple books on autonomous system engineering.
View Publications 
 Industry Standards
Key contributor to the definition of global safety and communication standards for autonomous vehicles through leadership roles in IEEE and ETSI.
View Standards Work 
Education
Ph.D. in Computer Science (Automotive Intelligent Systems)
University of the Basque Country, Spain & Clemson University, USA, 2012
Summa Cum Laude
Joint international doctoral program focusing on conversational AI for vehicles.
M.Sc. in Media and Information Technology
Hochschule Offenburg, Germany, 2009
Coursework in multimedia systems and human-computer interaction.
B.Sc. in Communication Sciences
University of Burgos, Spain, 2006
Summa Cum Laude
Minor in Telecommunications.
 Sitemap
{% include base_path %} A list of all the posts and pages found on the site. For you robots out there is an [XML version]({{ base_path }}/sitemap.xml) available for digesting as well.Pages
{% for post in site.pages %} {% include archive-single.html %} {% endfor %}Posts
{% for post in site.posts %} {% include archive-single.html %} {% endfor %} {% capture written_label %}'None'{% endcapture %} {% for collection in site.collections %} {% unless collection.output == false or collection.label == "posts" %} {% capture label %}{{ collection.label }}{% endcapture %} {% if label != written_label %}{{ label }}
{% capture written_label %}{{ label }}{% endcapture %} {% endif %} {% endunless %} {% for post in collection.docs %} {% unless collection.output == false or collection.label == "posts" %} {% include archive-single.html %} {% endunless %} {% endfor %} {% endfor %}
 Posts by Tags
{% include base_path %} {% include group-by-array collection=site.posts field="tags" %} {% for tag in group_names %} {% assign posts = group_items[forloop.index0] %}{{ tag }}
{% for post in posts %} {% include archive-single.html %} {% endfor %} {% endfor %}
 Talk map
This map is generated from a Jupyter Notebook file in /_talks/talkmap.ipynb, which mines the location fields in the .md files in _talks/.
 Talks and presentations
{% if site.talkmap_link == true %}See a map of all the places I've given a talk!
{% endif %} {% for post in site.talks reversed %} {% include archive-single-talk.html type='talk' %} {% endfor %}
 Talks
 {% include base_path %} {% assign postsByYear = site.talks | group_by_exp:"post", "post.date | date: '%Y'" | reverse %} {% for year_group in postsByYear %} <details class="year-details" {% if forloop.first %}open{% endif %}> {{ year_group.name }}
 {% for post in year_group.items reversed %} {% include archive-single-talk.html type='talk' %}
{% endfor %}
</details> {% endfor %}
 Teaching
{% include base_path %} {% for post in site.teaching reversed %} {% include archive-single.html %} {% endfor %}
 Teaching
 {% include base_path %} {% assign postsByYear = site.teaching | group_by_exp:"post", "post.date | date: '%Y'" | reverse %} {% for year_group in postsByYear %} <details class="year-details" {% if forloop.first %}open{% endif %}> {{ year_group.name }}
 {% for post in year_group.items reversed %} {% include archive-single-teaching.html type='teaching' %}
{% endfor %}
</details> {% endfor %}
 Terms and Privacy Policy
{% include base_path %} {% include toc %} ## Privacy Policy The privacy of my visitors is extremely important. This Privacy Policy outlines the types of personal information that is received and collected and how it is used. First and foremost, I will never share your email address or any other personal information to anyone without your direct consent. ### Log Files Like many other websites, this site uses log files to help learn about when, from where, and how often traffic flows to this site. The information in these log files include: * Internet Protocol addresses (IP) * Types of browser * Internet Service Provider (ISP) * Date and time stamp * Referring and exit pages * Number of clicks All of this information is not linked to anything that is personally identifiable. ### Cookies and Web Beacons When you visit this site "convenience" cookies are stored on your computer when you submit a comment to help you log in faster to [Disqus](http://disqus.com) the next time you leave a comment. Third-party advertisers may also place and read cookies on your browser and/or use web beacons to collect information. This site has no access or control over these cookies. You should review the respective privacy policies on any and all third-party ad servers for more information regarding their practices and how to opt-out. If you wish to disable cookies, you may do so through your web browser options. Instructions for doing so can be found on the specific web browsers' websites. #### Google Analytics Google Analytics is a web analytics tool I use to help understand how visitors engage with this website. It reports website trends using cookies and web beacons without identifying individual visitors. You can read [Google Analytics Privacy Policy](http://www.google.com/analytics/learn/privacy.html).
 Blog posts
{% include base_path %} {% capture written_year %}'None'{% endcapture %} {% for post in site.posts %} {% capture year %}{{ post.date | date: '%Y' }}{% endcapture %} {% if year != written_year %}{{ year }}
{% capture written_year %}{{ year }}{% endcapture %} {% endif %} {% include archive-single.html %} {% endfor %}
 
@import "jekyll-theme-primer";
 
 
 
 
 
 
 
 
 
{"/about/":"https://ignacioalvmar.com/","/about.html":"https://ignacioalvmar.com/","/md/":"https://ignacioalvmar.com/markdown/","/markdown.html":"https://ignacioalvmar.com/markdown/","/nmp/":"https://ignacioalvmar.com/non-menu-page/","/nmp.html":"https://ignacioalvmar.com/non-menu-page/","/cv/":"https://ignacioalvmar.com/resume/","/wordpress/blog-posts/":"https://ignacioalvmar.com/year-archive/"}
 Jupyter notebook markdown generator
# Jupyter notebook markdown generator These .ipynb files are Jupyter notebook files that convert a TSV containing structured data about talks (`talks.tsv`) or presentations (`presentations.tsv`) into individual markdown files that will be properly formatted for the academicpages template. The notebooks contain a lot of documentation about the process. The .py files are pure python that do the same things if they are executed in a terminal, they just don't have pretty documentation.
 
 {% if page.xsl %} {% endif %} {% assign collections = site.collections | where_exp:'collection','collection.output != false' %}{% for collection in collections %}{% assign docs = collection.docs | where_exp:'doc','doc.sitemap != false' %}{% for doc in docs %} {{ doc.url | replace:'/index.html','/' | absolute_url | xml_escape }} {% if doc.last_modified_at or doc.date %}{{ doc.last_modified_at | default: doc.date | date_to_xmlschema }} {% endif %} {% endfor %}{% endfor %}{% assign pages = site.html_pages | where_exp:'doc','doc.sitemap != false' | where_exp:'doc','doc.url != "/404.html"' %}{% for page in pages %} {{ page.url | replace:'/index.html','/' | absolute_url | xml_escape }} {% if page.last_modified_at %}{{ page.last_modified_at | date_to_xmlschema }} {% endif %} {% endfor %}{% assign static_files = page.static_files | where_exp:'page','page.sitemap != false' | where_exp:'page','page.name != "404.html"' %}{% for file in static_files %} {{ file.path | replace:'/index.html','/' | absolute_url | xml_escape }} {{ file.modified_time | date_to_xmlschema }}  {% endfor %}
 
Sitemap: {{ "sitemap.xml" | absolute_url }}
 
{% if page.xsl %}{% endif %}<feed xmlns="http://www.w3.org/2005/Atom" {% if site.lang %}xml:lang="{{ site.lang }}"{% endif %}>Jekyll<link href="{{ '/' | absolute_url }}" rel="alternate" type="text/html" {% if site.lang %}hreflang="{{ site.lang }}" {% endif %}/>{{ site.time | date_to_xmlschema }}{{ page.url | absolute_url | xml_escape }}{% assign title = site.title | default: site.name %}{% if page.collection != "posts" %}{% assign collection = page.collection | capitalize %}{% assign title = title | append: " | " | append: collection %}{% endif %}{% if page.category %}{% assign category = page.category | capitalize %}{% assign title = title | append: " | " | append: category %}{% endif %}{% if title %}{{ title | smartify | xml_escape }}{% endif %}{% if site.description %}{{ site.description | xml_escape }}{% endif %}{% if site.author %}{{ site.author.name | default: site.author | xml_escape }}{% if site.author.email %}{{ site.author.email | xml_escape }}{% endif %}{% if site.author.uri %}{{ site.author.uri | xml_escape }}{% endif %}{% endif %}{% if page.tags %}{% assign posts = site.tags[page.tags] %}{% else %}{% assign posts = site[page.collection] %}{% endif %}{% if page.category %}{% assign posts = posts | where: "categories", page.category %}{% endif %}{% unless site.show_drafts %}{% assign posts = posts | where_exp: "post", "post.draft != true" %}{% endunless %}{% assign posts = posts | sort: "date" | reverse %}{% assign posts_limit = site.feed.posts_limit | default: 10 %}{% for post in posts limit: posts_limit %}<entry{% if post.lang %}{{" "}}xml:lang="{{ post.lang }}"{% endif %}>{% assign post_title = post.title | smartify | strip_html | normalize_whitespace | xml_escape %}{{ post_title }}{{ post.date | date_to_xmlschema }}{{ post.last_modified_at | default: post.date | date_to_xmlschema }}{{ post.id | absolute_url | xml_escape }}{% assign excerpt_only = post.feed.excerpt_only | default: site.feed.excerpt_only %}{% unless excerpt_only %}<![CDATA[{{ post.content | strip }}]]>{% endunless %}{% assign post_author = post.author | default: post.authors[0] | default: site.author %}{% assign post_author = site.data.authors[post_author] | default: post_author %}{% assign post_author_email = post_author.email | default: nil %}{% assign post_author_uri = post_author.uri | default: nil %}{% assign post_author_name = post_author.name | default: post_author %}{{ post_author_name | default: "" | xml_escape }}{% if post_author_email %}{{ post_author_email | xml_escape }}{% endif %}{% if post_author_uri %}{{ post_author_uri | xml_escape }}{% endif %}{% if post.category %}{% elsif post.categories %}{% for category in post.categories %}{% endfor %}{% endif %}{% for tag in post.tags %}{% endfor %}{% assign post_summary = post.description | default: post.excerpt %}{% if post_summary and post_summary != empty %}<![CDATA[{{ post_summary | strip_html | normalize_whitespace }}]]>
{% endif %}{% assign post_image = post.image.path | default: post.image %}{% if post_image %}{% unless post_image contains "://" %}{% assign post_image = post_image | absolute_url %}{% endunless %}{% endif %}</entry>{% endfor %}</feed>

Posts by Category

Posts by Collection

No venue listed (2024)

PDF

BibTeX

@article{liuyang2024misbehaviordete,
  author    = {Liuyang Yang et al.},
  title     = {Misbehavior detection using data consistency checks for collective perception messages},
  journal  = {No venue listed},
  year      = {2024}
}

portfolio

Portfolio item number 1

This is an item in your portfolio. It can be have images or nice text. If you name the file .md, it will be parsed as markdown. If you name the file .html, it will be parsed as HTML.

Portfolio item number 2

This is an item in your portfolio. It can be have images or nice text. If you name the file .md, it will be parsed as markdown. If you name the file .html, it will be parsed as HTML.

publications

Voice interfaced vehicle user help

Published in Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 2010

Abstract

Manuals were designed to provide support and information about the usage and maintenance of the vehicle. In many cases user’s manuals are the driver’s only guidance. However, lack of clarity and efficiency of manuals lead to user dissatisfaction. In vehicles this problem is even more crucial given that driving a motor vehicle is, for many people, the most complex and potentially dangerous task they will perform during their lifetime. In this paper we present a voice interfaced driver manual that can potentially fix the deficiencies of its alternatives. In addition we aim to provide a case for the integration of such technology in a vehicle to reduce driver distraction, increase driver satisfaction, and manual usability, while also benefiting Original Equipment Manufacturers (OEMs) in lowering costs and reducing the documentation process.

Key Contributions

Voice-interfaced user help system design
Conversational query methodology
Dynamic voice user interface architecture
Prototype development and database design
Context-aware system and architecture vision

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Citation

@article{ignacio2010voiceinterfaced, author = {Ignacio Alvarez et al.}, title = {Voice interfaced vehicle user help}, journal = {Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, year = {2010} }

Alvarez, I., et al. (2010). Voice interfaced vehicle user help. Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

Are educational video games all they are cracked up to be

Published in ‘International Journal of Learning Technology’, 2011

Abstract

This paper investigates the benefits of learning from educational video games compared to learning by reading from a text document. The participants were exposed to Lewis and Clark expedition via a video game or text document. During the learning task, playing the game or reading, participants wore a Brain Computer Interface (BCI) device to gather their level of engagement. After the learning sessions, post-experiment questionnaires were used to assess the amount of information retained after each session. The results of this study suggests that the educational video games might not be significantly engaging, and also that learning by reading a handout may be better for retaining information. Furthermore, this paper briefly discusses the BCI device, and how it can be used to measure engagement of the participants.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

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Citation

@inproceedings{andujar2011educational, title={Are educational video games all they're cracked up to be?: A physiological approach for measuring engagement in educational video games vs. conventional learning techniques}, author={Andujar, Marvin and Ekandem, Josh and Alvarez, Ignacio and James, Melva and Gilbert, Juan}, booktitle={eLearn: World Conference on EdTech}, pages={539--544}, year={2011}, organization={Association for the Advancement of Computing in Education (AACE)} }

Andujar, M., Ekandem, J., Alvarez, I., James, M., & Gilbert, J. (2011). Are educational video games all they're cracked up to be?: A physiological approach for measuring engagement in educational video games vs. conventional learning techniques. eLearn: World Conference on EdTech, 539-544.

Autonui a workshop on automotive natural user interfaces

Published in ‘Proceedings of AutomotiveUI 2011’, ACM 2011

Abstract

Natural user interfaces by means of gesture and speech interaction have become a hot topic in research as well as already for real products. Most use cases currently center around consumer electronics devices like smart phones, TV sets, gaming, or other large screens like tabletops. Motivated by the latest results in those areas, our vision is to apply natural user interfaces like gesture and speech interaction to the automotive domain as well. This integration might on one hand reduce driver distraction in certain cases and on the other hand allow to design new experiences for operating infotainment and entertainment systems. The goal of this workshop is to explore the design space of natural multi-modal automotive user interfaces. We would like to analyze where and how new interaction techniques can be integrated into the car.

Key Contributions

Natural User Interfaces in Automotive
Workshop organization and community building

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Citation

@inproceedings{pfleging2011autonui, title={Autonui: a workshop on automotive natural user interfaces}, author={Pfleging, Bastian and Schmidt, Albrecht and D{"o}ring, Tanja and Knobel, Martin}, booktitle={Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages={207--209}, year={2011} }

Pfleging, B., Schmidt, A., Döring, T., & Knobel, M. (2011). Autonui: a workshop on automotive natural user interfaces. Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 207-209.

Designing driver centric natural voice user interfaces

Published in Adjunct Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 2011

Abstract

The current growth of automotive electronics aims toextend vehicle functionality and information access. Thispaper explores the application of Natural Voice UserInterfaces as a preferred interaction modality with in-vehicle technologies to lower driver distraction effects andimprove the user experience. The benefits and risks ofnatural speech interactions are evaluated in order topropose a driver-centric design guideline based on previousresearch. The paper concludes that driving scenarios canprofit considerably from systems that apply natural speechinterfaces to allow the driver to access information.

Key Contributions

Natural Voice Interaction
Voice-interfaced user help system design
Conversational Dialogue Managment
Context-aware system and architecture vision

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Citation

@inproceedings{alvarez2011designing, title={Designing driver-centric natural voice user interfaces}, author={Alvarez, Ignacio and Martin, Aqueasha and Dunbar, Jerone and Taiber, Joachim and Wilson, Dale-Marie and Gilbert, Juan E}, booktitle={Adjunct Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications. online}, pages={156--159}, year={2011} }

Alvarez, Ignacio and Martin, Aqueasha and Dunbar, Jerone and Taiber, Joachim and Wilson, Dale-Marie and Gilbert, Juan E (2011). Designing driver centric natural voice user interfaces. Adjunct Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications. online.

iHelp, the Ubiquitous Vehicle User Help

Published in Proceedings of Interact 2011, 2011

Abstract

Voice-interfaced, in-vehicle assistance includes receiving a Voice-based query from a user in the vehicle, and then determining at least one of a user emotional state, user expertise level and speech recognition confidence level associated with the Voice-based query. A text-based query may then be derived from the Voice-based query, and used to search a help database for answers corresponding to the Voice-based query. At least one response is then provided to the user in the form of Voice-based assistance in accordance with at least one of the user emotional state, user expertise level and speech recognition confidence level.

Key Contributions

Voice-interfaced user help
Conversational dialogue management
Prototype development

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Citation

@inproceedings{alvarez2011ihelp, title={iHelp, the Ubiquitous Vehicle User Help}, author={Alvarez, Ignacio and Fischer, Hans-Peter}, booktitle={Proceedings of Interact}, pages={5--9}, year={2011} }

Alvarez, I. (2011). . Proceedings of Interact.

AutoNUI: 2nd Workshop on Automotive Natural User Interfaces

Published in ‘Adjunct Proceedings of the 4th International Conference on Automotive User Interfaces, ACM 2012

Abstract

Natural user interfaces—generally based on gesture and speech interaction—are an increasingly hot topic in research and are already being applied in a multitude of commercial products. Most use cases currently involve consumer electronics devices like smart phones, tablets, TV sets, game consoles, or large-screen tabletop computers.Motivated by the latest results in those areas, our vision is to apply natural user interfaces, for example gesture and conversational speech interaction, to the automotive domain as well. This integration might on one hand reduce driver distraction in certain cases and on the other hand might allow the design of new user experiences for infotainment and entertainment systems. The goal of this workshop is to explore the design space of natural multi-modal automotive user interfaces and to continue the fruitful discussions held at the 1st Workshop on Automotive Natural User Interfaces from AutomotiveUI ’11 in Salzburg, Austria. We would like to analyze where and how new interaction techniques can be integrated into the car.

Key Contributions

Workshop organization and community building

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Citation

@inproceedings{pflegling2012autonui, title={AutoNUI: 2nd Workshop on Automotive Natural User Interfaces}, author={Pflegling, Bastian and D{"o}ring, Tanja and Alvarez, Ignacio and Kranz, Matthias and Weinberg, Garrett and Healey, Jennifer}, booktitle={International Conference on Automotive User Interfaces and Interactive Vehicular Applications: 17/10/2012-19/10/2012}, year={2012} }

Pflegling, Bastian and D{"o (2012). AutoNUI: 2nd Workshop on Automotive Natural User Interfaces. International Conference on Automotive User Interfaces and Interactive Vehicular Applications: 17/10/2012-19/10/2012.

Contribution to the development of intelligent conversational assistants in automotive environments

Published in EHU Scientific Production Portal, EHU 2012

Abstract

This thesis presents the design and development of the Conversational Automotive Assistant, Voice User Help, a conversational system based on the question-answer paradigm and designed to consult vehicle documentation while driving. This work compiles research in the fields of technical documentation, information retrieval, natural language processing, vehicle user interface design, user experience, and affective computing with the goal of creating an adaptive and dynamic assistant that modifies its conversational behavior depending on the user’s emotional state.

Key Contributions

Conversational Asistant for In-cabin Automotive Applications
Information Retrieval Algorithms
Human-Computer Interaction Voice Interfaces
Automotive Context-aware Affective Computing

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Citation

@phdthesis{martinez2012contribution, title={Contribution to the development of intelligent conversational assistants in automotive environments}, author={Mart{\'\i}nez, Ignacio Javier {\'A}lvarez}, year={2012}, school={Universidad del Pa{\'\i}s Vasco-Euskal Herriko Unibertsitatea} }

Alvarez, I. (2012). . .

Emotional Adaptive Vehicle User Interfaces, Moderating negative effects of failed technology interactions while driving

Published in ‘Adjunct Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI 2012)’, 2012

Abstract

Automotive Natural User Interfaces have the potential to increase user experience providing intuitive interactions for drivers. However, in the complex setting of a driving vehicle, failed interactions with in-vehicle technology can lead to frustration and put drivers in a dangerous situation. This paper evaluates the possibility of applying emotion recognition to vehicular spoken dialogue systems in order to adapt the dialog strategies, in error recovery scenarios. An emotional taxonomy is developed for the interactions with a conversational vehicular application, the Voice User Help. The positive results of the performance of VUH emotion recognizer support the creation of real-time classification of the user emotional state, which serves as basis to emotional reappraisal dialog strategies that mitigate negative effects on the driver’s cognitive load and driver performance.

Key Contributions

Developed the Voice User Help system
Created a VUH-specific emotional taxonomy
Built a real-time emotion recognizer
Proposed adaptive emotional dialog strategies
Used emotion for error recovery

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Citation

@inproceedings{alvarez2012emotional, title={Emotional Adaptive Vehicle User Interfaces: moderating negative effects of failed technology interactions while driving}, author={Alvarez, Ignacio and Lopez-de Ipi{\~n}a, Karmele and Daily, Shaundra B and Gilbert, Juan E}, booktitle={Proceedings of Workshop of Automotive Natural Interfaces, together with International Conference on Automotive User Interfaces}, pages={57--60}, year={2012} }

Alvarez, Ignacio and Lopez-de Ipi{\~n (2012). Emotional Adaptive Vehicle User Interfaces, Moderating negative effects of failed technology interactions while driving. Proceedings of Workshop of Automotive Natural Interfaces, together with International Conference on Automotive User Interfaces.

Evaluating the ergonomics of BCI devices for research and experimentation

Published in Journal of Ergonomics, Taylor & Francis Online 2012

Abstract

The use of brain computer interface (BCI) devices in research and applications has exploded in recent years. Applications such as lie detectors that use functional magnetic resonance imaging (fMRI) to video games controlled using electroencephalography (EEG) are currently in use. These developments, coupled with the emergence of inexpensive commercial BCI headsets, such as the Emotiv EPOC ( http://emotiv.com/index.php) and the Neurosky MindWave, have also highlighted the need of performing basic ergonomics research since such devices have usability issues, such as comfort during prolonged use, and reduced performance for individuals with common physical attributes, such as long or coarse hair. This paper examines the feasibility of using consumer BCIs in scientific research. In particular, we compare user comfort, experiment preparation time, signal reliability and ease of use in light of individual differences among subjects for two commercially available hardware devices, the Emotiv EPOC and the Neurosky MindWave. Based on these results, we suggest some basic considerations for selecting a commercial BCI for research and experimentation.

Statement of Relevance: Despite increased usage, few studies have examined the usability of commercial BCI hardware. This study assesses usability and experimentation factors of two commercial BCI models, for the purpose of creating basic guidelines for increased usability. Finding that more sensors can be less comfortable and accurate than devices with fewer sensors.

Key Contributions

BCI evaluation
BCI Signal Limitation Modelling
Practical Use recommendations

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Citation

@article{ekandem2012evaluating, title={Evaluating the ergonomics of BCI devices for research and experimentation}, author={Ekandem, Joshua I and Davis, Timothy A and Alvarez, Ignacio and James, Melva T and Gilbert, Juan E}, journal={Ergonomics}, volume={55}, number={5}, pages={592--598}, year={2012}, publisher={Taylor \& Francis} }

Alvarez, I. (2012). . Ergonomics.

The voice user help, a smart vehicle assistant for the elderly

Published in Proceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence, Springer 2012

Abstract

The rapid advancement of vehicular technologies has resulted in an exponential increase of new vehicle functions road assistance and connected vehicles services. Vehicle manuals are designed to provide support and information about the use and maintenance of these features. However, current vehicle user manuals do not allow looking for information under driving conditions. This paper presents the Voice User Help, a smart voice-operated system that utilizes natural language understanding and emotional adaptive interfaces to assist drivers when looking for vehicle information with minimal effect on their driving performance. Additionally, the system presents an opportunity for elder drivers to reduce the learning curve of new in-vehicle technologies and improve efficiency. Results on user acceptance of the Voice User Help, as well as cognitive load and driver distraction effects generated during a simulated drive indicate that the Voice User help is an extremely desirable feature and potentially safe application since it did not significantly decrement driving performance. Furthermore preliminary results on adaptive voice interfaces using emotion recognition indicate that personalization of the interaction will be able to palliate possible negative effects that happen during system error recovery.

Key Contributions

Developed voice-operated vehicle help system
Aimed to assist elderly drivers
Evaluated driver distraction and usability
Integrated emotion recognition for adaptation
Created a VUH-specific emotional taxonomy

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BibTeX

Driver in the loop: Best practices in automotive sensing and feedback mechanisms

Published in ‘Automotive user interfaces: creating interactive experiences in the car’, 2017

Abstract

Given the rapid advancement of technologies in the automotive domain, driver–vehicle interaction has recently become more and more complicated. The amount of research applied to the vehicle cockpit is increasing, with the advent of (highly) automated driving, as the range of interaction that is possible in a driving vehicle expands. However, as opportunities increase, so does the number of challenges that automotive user experience designers and researchers will face. This chapter focuses on the instrumentation of sensing and displaying techniques and technologies to make better user experience while driving. In the driver–vehicle interaction loop, the vehicle can sense driver states, analyze, estimate, and model the data, and then display it through the appropriate channels for intervention purposes. To improve the interaction, a huge number of new/affordable sensing (EEG, fNIRS, IR imaging) and feedback (head-up displays, auditory feedback, tactile arrays, etc.) techniques have been introduced. However, little research has attempted to investigate this area in a systematic way. This chapter provides an overview of recent advances of input and output modalities to be used for timely, appropriate driver–vehicle interaction. After outlining relevant background, we provide information on the best-known practices for input and output modalities based on the exchange results from the workshop on practical experiences for measuring and modeling drivers and driver–vehicle interactions at AutomotiveUI 2015. This chapter can help answer research questions on how to instrument a driving simulator or realistic study to gather data and how to place interaction outputs to enable appropriate driver interactions.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

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Citation

@incollection{riener2017driver, title={Driver in the loop: Best practices in automotive sensing and feedback mechanisms}, author={Riener, Andreas and Jeon, Myounghoon and Alvarez, Ignacio and Frison, Anna K}, booktitle={Automotive user interfaces: creating interactive experiences in the car}, pages={295--323}, year={2017}, publisher={Springer} }

Riener, Andreas and Jeon, Myounghoon and Alvarez, Ignacio and Frison, Anna K (2017). Driver in the loop: Best practices in automotive sensing and feedback mechanisms. Automotive user interfaces: creating interactive experiences in the car.

Skyline: A Platform Towards Scalable UX-Centric In-Vehicle HMI Development

Published in ‘International Journal of Mobile Human Computer Interaction (IJMHCI)’, 2017

Abstract

This paper describes the research and development process of an in-vehicle user experience using Skyline, an automotive prototyping platform created in Intel Labs to empower interaction designers and user experience researches to rapidly and iteratively develop and test in-vehicle user experience concepts. The paper describes the hardware and software components of Skyline in depth and how to configure them to suit individual researcher needs. The paper also presents a case study to exemplify the design making process that Skyline enables. From ideation to use-case creation, prototyping and validation through user assessment, the paper showcases the benefits of capturing early qualitative user feedback as support for rapid prototyping walking through a study titled Agency vs. Control and the associated interactions inside the cockpit. Ten defined use-cases are developed and integrated into a hero scenario in Skyline. High fidelity HMI concepts are tested and validated over the course of six months with feedback from a total of fifty users.

Key Contributions

Automotive HMI Prototyping
Rapid HCI User Interface Design
Evaluation Methods

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Citation

@article{alvarez2017skyline, title={Skyline: A Platform Towards Scalable UX-Centric In-Vehicle HMI Development}, author={Alvarez, Ignacio and Rumbel, Laura}, journal={International Journal of Mobile Human Computer Interaction (IJMHCI)}, volume={9}, number={3}, pages={34--53}, year={2017}, publisher={IGI Global Scientific Publishing} }

Alvarez, Ignacio and Rumbel, Laura (2017). Skyline: A Platform Towards Scalable UX-Centric In-Vehicle HMI Development. International Journal of Mobile Human Computer Interaction (IJMHCI).

Socializing under the influence of distracted driving: a study of the effects of in-vehicle and outside-of-the-vehicle communication while driving

Published in ‘Springer Nature Advances in Human Aspects of Transportation’, Springer 2017

Abstract

Advancements of in-vehicle technologies and the development of mobile applications that keep a driver connected in a driving environment have caused an increasingly dangerous safety concern. Distracted driving has gained the attention of legislators and governments globally. Countries have constituted bans that partially or fully forbid drivers from using gadgets while driving, especially hindering out-of-the-vehicle communications. This paper introduces Voiceing™, a voice-activated application meant to improve social communications in the car, serving as a safe alternative to distracted driving. Other modalities of interaction such as texting, in-vehicle conversations and outside-of-the-vehicle conversation have been measured and compared with Voiceing™ investigating effects on driver’s performance, cognitive load and user acceptance. Results from this study suggest that Voiceing™ is a safer alternative than in-vehicle interactions with humans. Results also show that natural speech interaction of in-vehicle applications and the inclusion of context awareness help improve driving performance while interacting with a vehicle system.

Key Contributions

Introduces Voiceing™ a voice-activated application
Compares various in-car communication methods
Measures performance, cognitive load, acceptance
Voiceing™ is safer than human interactions
Natural speech improves driving performance

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Citation

@inproceedings{alnizami2016socializing, title={Socializing under the influence of distracted driving: a study of the effects of in-vehicle and outside-of-the-vehicle communication while driving}, author={Alnizami, Hanan and Alvarez, Ignacio and Gilbert, Juan E}, booktitle={Advances in Human Aspects of Transportation: Proceedings of the AHFE 2016 International Conference on Human Factors in Transportation, July 27-31, 2016, Walt Disney World{\textregistered}, Florida, USA}, pages={243--255}, year={2016}, organization={Springer} }

Alnizami, Hanan and Alvarez, Ignacio and Gilbert, Juan E (2016). Socializing under the influence of distracted driving: a study of the effects of in-vehicle and outside-of-the-vehicle communication while driving. Advances in Human Aspects of Transportation: Proceedings of the AHFE 2016 International Conference on Human Factors in Transportation, July 27-31, 2016, Walt Disney World{\textregistered.

The insight–prototype–product cycle best practices and processes to iteratively advance in-vehicle interactive experiences development

Published in Automotive User Interfaces: Creating Interactive Experiences in the Car, Springer Book, Springer 2017

Abstract

In-vehicle experiences are made up mainly of mundane small moments, repeated practices, and taken-for-granted decisions that make up daily experiences in and around private passenger vehicles. Understanding what those experiences are for drivers around the world presents an opportunity for designing novel interactive experiences, technologies, and user interfaces for vehicles. In this chapter, we present a set of tools, methodologies, and practices that will help reader create a holistic design space for future mobility. Transitioning between ethnography, insights, prototyping, experience design, and requirements decomposition is a challenging task even for experienced UX professionals. This chapter provides guidance in this matter with practical examples.

Key Contributions

Ethnographic research methods for Automotive System Development
Rapid prototyping methods for Automotive Functions
Experience Design mapping to product specifications
Product Development evaluation and validation
Product Success metrics

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Citation

@incollection{alvarez2017insight, title={The insight--prototype--product cycle best practices and processes to iteratively advance in-vehicle interactive experiences development}, author={Alvarez, Ignacio and Jordan, Adam and Knopf, Juliana and LeBlanc, Darrell and Rumbel, Laura and Zafiroglu, Alexandra}, booktitle={Automotive User Interfaces: Creating Interactive Experiences in the Car}, pages={377--400}, year={2017}, publisher={Springer} }

Alvarez, I. (2017). . Automotive User Interfaces: Creating Interactive Experiences in the Car.

Towards Adaptive Ambient In-Vehicle Displays and Interactions: Insights and Design Guidelines from the 2015 AutomotiveUI Dedicated Workshop

Published in ‘Automotive User Interfaces: Creating Interactive Experiences in the Car’, ACM 2017

Abstract

Informing a driver of a vehicle’s changing state and environment is a major challenge that grows with the introduction of in-vehicle assistant and infotainment systems. Even in the age of automation, the human will need to be in the loop for monitoring, taking over control, or making decisions. In these cases, poorly designed systems could lead to needless attentional demands imparted on the driver, taking it away from the primary driving task. Existing systems are offering simple and often unspecific alerts, leaving the human with the demanding task of identifying, localizing, and understanding the problem. Ideally, such systems should communicate information in a way that conveys its relevance and urgency. Specifically, information useful to promote driver safety should be conveyed as effective calls for action, while information not pertaining to safety (therefore less important) should be conveyed in ways that do not jeopardize driver attention. Adaptive ambient displays and peripheral interactions have the potential to provide superior solutions and could serve to unobtrusively present information, to shift the driver’s attention according to changing task demands, or enable a driver to react without losing the focus on the primary task. In order to build a common understanding across researchers and practitioners from different fields, we held a Workshop on Adaptive Ambient In-Vehicle Displays and Interactions at the AutomotiveUI`15 conference. In this chapter, we discuss the outcomes of this workshop, provide examples of possible applications now or in the future and conclude with challenges in developing or using adaptive ambient interactions.

Key Contributions

Workshop organization and community building
Human-computer interaction design

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Citation

@Inbook{Löcken2017, author={Löcken, Andreas and Sadeghian Borojeni, Shadan and Müller, Heiko and Gable, Thomas M. and Triberti, Stefano and Diels, Cyriel and Glatz, Christiane and Alvarez, Ignacio and Chuang, Lewis and Boll, Susanne}, editor={Meixner, Gerrit and Müller, Christian}, title={Towards Adaptive Ambient In-Vehicle Displays and Interactions: Insights and Design Guidelines from the 2015 AutomotiveUI Dedicated Workshop}, booktitle={Automotive User Interfaces: Creating Interactive Experiences in the Car}, year={2017}, publisher={Springer International Publishing}, address={Cham}, pages={325--348}, isbn={978-3-319-49448-7}, doi={10.1007/978-3-319-49448-7_12}, url={https://doi.org/10.1007/978-3-319-49448-7_12} }

Löcken, A., Sadeghian Borojeni, S., Müller, H., Gable, T. M., Triberti, S., Diels, C., Glatz, C., Alvarez, I., Chuang, L., & Boll, S. (2017). Towards Adaptive Ambient In-Vehicle Displays and Interactions: Insights and Design Guidelines from the 2015 AutomotiveUI Dedicated Workshop. In G. Meixner & C. Müller (Eds.), Automotive User Interfaces: Creating Interactive Experiences in the Car (pp. 325-348). Springer International Publishing.

Workshop on user-centered design for automated driving systems

Published in Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, ACM 2017

Abstract

Automated driving systems (ADS) are mainly regarded from an innovation and technology-centered perspective. In academia, as well as in industry, there is a concentration on technical issues to maintain competitiveness while aspects like acceptance, trust and user experience are widely under-researched. However, the “human factor” is critical for a comprehensive establishment of ADS technology on the market. We believe that there is a need to focus on a user-centered design (UCD) perspective to bring ADS innovation to a next level and to achieve a wide acceptance in society. In this workshop we want to discuss special requirements of UCD applied to ADS, to address challenges and opportunities and to reveal new research fields for future work.

Key Contributions

Proposes migration-tolerant ATC display concept
Advocates for stepwise functionality introduction
Compares small versus large integration steps
Measures usability, learnability, and acceptance

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Citation

@inproceedings{10.1145/3131726.3131734, author = {Frison, Anna-Katharina and Pfleging, Bastian and Riener, Andreas and Jeon, Myounghoon Philart and Alvarez, Ignacio and Ju, Wendy}, title = {Workshop on User-Centered Design for Automated Driving Systems}, year = {2017}, isbn = {9781450351515}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3131726.3131734}, doi = {10.1145/3131726.3131734}, booktitle = {Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications Adjunct}, pages = {22–27}, numpages = {6}, keywords = {User-centered design, Automotive user interfaces, Automated driving systems}, location = {Oldenburg, Germany}, series = {AutomotiveUI '17} }

Alvarez, I. (2017). . Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications Adjunct.

An international survey on automated and electric vehicles: Austria, Germany, South Korea, and USA

Published in ‘Springer Natural Digital Human Modelling’, Springer 2018

Abstract

As development of automated vehicles and adoption of electric vehicles continue to grow, there is an increasing interest in the public opinions on these technologies. We conducted an international online survey to gather information about people’s hopes and concerns for automated and electric vehicles from a total of 866 people from four countries – Austria, Germany, South Korea, and USA. Results revealed some differences across countries in the perceptions of automated and electric vehicles. However, differences between the same countries have shrunk compared to our previous survey completed in 2012. Results are discussed with limitations and future work.

Key Contributions

Automated driving system development
Electric Vehicles
International Trends

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@inproceedings{jeon2018international, title={An international survey on automated and electric vehicles: Austria, Germany, South Korea, and USA}, author={Jeon, Myounghoon and Riener, Andreas and Sterkenburg, Jason and Lee, Ju-Hwan and Walker, Bruce N and Alvarez, Ignacio}, booktitle={International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management}, pages={579--587}, year={2018}, organization={Springer} }

Jeon, Myounghoon and Riener, Andreas and Sterkenburg, Jason and Lee, Ju-Hwan and Walker, Bruce N and Alvarez, Ignacio (2018). An international survey on automated and electric vehicles: Austria, Germany, South Korea, and USA. International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management.

ARV 2018: 2nd Workshop on Augmented Reality for Intelligent Vehicles

Published in ‘Adjunct Proceedings of the 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’18)’, ACM 2018

Abstract

Augmented reality (AR) has the potential to improve road safety, support more immersive (non-) driving related activities, and finally enhance driving experience. AR may also be the enabling technology to help on the transition towards automated driving. However, augmented reality still faces a number of technical challenges when applied in vehicles, and also several human factors issues need to be solved. In this workshop, we will discuss potential and constraints as well as impact, role, and adequacy of AR in driving applications. The primary goal of this workshop is to define a research agenda for the use of AR in intelligent vehicles within the next 3 to 5 years.

Key Contributions

Workshop organization and community building
Defines a 3-5 year collaborative research agenda
Identifies four key AR problem fields
Discusses practical usage of AR devices
Outlines research challenges and hypotheses

In-car emotion detection and regulation have become an emerging and important branch of research within the automotive domain. Different emotional states can greatly influence human driving performance and user experience both in manual and automated driving conditions. The monitoring and regulation of relevant emotional states is therefore important to avoid critical driving scenarios with the human driver being in charge, and to ensure comfort and acceptance in autonomous driving. In this workshop we want to discuss the empathic user interface research to address challenges and opportunities and to reveal new research directions for future work. This workshop provides a forum for exchange and discussion on empathic user interfaces, including methods for emotion recognition and regulation, empathic automotive human-machine interaction design, user evaluation and measurements, and subsequent improvement of autonomous driving experience.

Key Contributions

Discusses empathic in-car user interfaces
Identifies critical scenarios and emotions
Explores emotion recognition and regulation methods
Uses 4mat system for workshop structure
Creates roadmap for future research

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@inproceedings{10.1145/3239092.3239098, author = {Bosch, Esther and Oehl, Michael and Jeon, Myounghoon and Alvarez, Ignacio and Healey, Jennifer and Ju, Wendy and Jallais, Christophe}, title = {Emotional GaRage: A Workshop on In-Car Emotion Recognition and Regulation}, year = {2018}, isbn = {9781450359474}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3239092.3239098}, doi = {10.1145/3239092.3239098}, booktitle = {Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages = {44–49}, numpages = {6}, keywords = {user acceptance, empathic vehicles, emotion recognition and regulation, Driver state assessment}, location = {Toronto, ON, Canada}, series = {AutomotiveUI '18} }

Bosch, Esther and Oehl, Michael and Jeon, Myounghoon and Alvarez, Ignacio and Healey, Jennifer and Ju, Wendy and Jallais, Christophe (2018). Emotional GaRage: A workshop on in-car emotion recognition and regulation. Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

“Peace of Mind”, An Experiential Safety Framework for Automated Driving Technology Interactions

Published in Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), IEEE 2018

Abstract

Current automated driving systems assume drivers continuously monitor the vehicle. Meanwhile, fully automated vehicles aim at not requiring human intervention for their safely operation. The industry is currently debating how these novel systems can be certified under functional safety standards. In this paper, we argue that the current safety picture is not comprehensive enough, since it alienates users. We propose experiential safety as a complement to existing functional safety and to develop a framework for experiential safety interactions between the user and automation in automated driving environments. To support the experiential safety design model, we provide an overview of the user-centered research on experiential automation safety, which includes results from online surveys, personal interviews, and gamified group workshops. We explore current user behaviors by focusing on what makes them feel safe as drivers and passengers, and how unexpected events and automation responses might impact their perception of safety. Among the highlighted results, we show how mismatched expectations and unexpected behaviors from autonomous vehicles can lead to frustration and compromised trust. We also show how automation feedback to the user can generate stress and anxiety if not properly configured and how a cooperative relationship between automation and the driver leads to more satisfying driving experiences. Finally, we present guidelines for the experiential safety to be applied by automotive engineers and designers in their development of automated driving technologies.

Key Contributions

Proposes an experiential safety concept for AVs
Develops a novel gamified workshop method
Presents an experiential safety framework
Defines user-lenses and machine-lenses guidelines
Finds collaborative feedback improves user emotion

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@INPROCEEDINGS{8569686, author={Strano, Marina and Novak, Fabricio and Walbert, Shelly and Palmeiro, Beatriz and Morales, Sonia and Alvarez, Ignacio}, booktitle={2018 21st International Conference on Intelligent Transportation Systems (ITSC)}, title={“Peace of Mind”, An Experiential Safety Framework for Automated Driving Technology Interactions}, year={2018}, volume={}, number={}, pages={53-59}, keywords={Safety;Automation;Interviews;Automobiles;Guidelines;Task analysis;Automated Driving;Experiential Safety;User experience design;design research;design theory;design science;methodology design;emerging technology}, doi={10.1109/ITSC.2018.8569686} }

Alvarez, I. (2018). . 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

Theo, take a right… uh… left: Conversational Route Negotiations with Autonomous Driving Assistants

Published in Adjunct Proceedings of the 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’18), ACM 2018

Abstract

We foresee conversational driver assistants playing a crucial role in automated driving interactions. In this video we present a study of user interactions with an in-vehicle agent, “Theo”, under SAE Level 4 automated driving. We use a remote Wizard-of-Oz setup where participants, sitting in a driving simulator, experience real-life video footage transmitted from a vehicle in the neighborhood and interact with Theo to instruct the vehicle where to go. We configured Theo to present 3 levels of conversational abilities (terse, verbose and helpful). We show the results of 9 participants tasked to negotiate destinations and route changes. Voice interaction was reported as preferred means of communication with Theo. There was a clear preference for talkative assistants which were perceived more responsive and intelligent. We highlight challenging interactions for users such as vehicle maneuvers in parking areas and specifying drop off points and interesting associations between the agent performance and the automated vehicle abilities.

Key Contributions

Automated driving system development
Pioneers a remote AI Wizard-of-Oz setup
Tests three conversational agent styles
Finds user preference for talkative agents
Identifies challenging user interaction scenarios
Links agent performance to perceived safety

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@inproceedings{10.1145/3239092.3267414, author = {Ekandem, Joshua E. and Alvarez, Ignacio and Rayburn, Cat and Johnson, Andrea}, title = {Theo, take a right ... uh ... left: Conversational Route Negotiations with Autonomous Driving Assistants}, year = {2018}, isbn = {9781450359474}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3239092.3267414}, doi = {10.1145/3239092.3267414}, booktitle = {Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages = {261–262}, numpages = {2}, keywords = {Advanced Driver Assistance Systems, Automated Driving, Conversational Dialogues, Natural Language Understanding, Route-Based Negotiation}, location = {Toronto, ON, Canada}, series = {AutomotiveUI '18} }

Ekandem, Joshua E. and Alvarez, Ignacio and Rayburn, Cat and Johnson, Andrea (2018). Theo, take a right... uh... left: Conversational Route Negotiations with Autonomous Driving Assistants. Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

Towards understanding emotional reactions of driver-passenger dyads in automated driving

Published in Proceedings of the 13th IEEE International Conference on Automatic Face & Gesture Recognition, IEEE 2018

Abstract

Automated driving has the potential to reduce the amount of fatal crashes, lighten the burden of commutes, and democratize mobility access to wider populations. But delegation of control to automation is not without issues. One of the foreseen drawbacks is that users might experience negative emotional reactions to unanticipated or unexplainable automated maneuvers. In this paper we present a novel method to induce targeted emotional reactions, frustration and startle, in simulated automated driving environments. We describe the data collection process for 17 driver - passenger dyads and discuss the data labelling method for generating reliable novel emotion datasets. This contribution is a foundational methodology towards expanding emotional understanding in automated vehicles, a critical skill for building long-term trusted experiences.

Key Contributions

Presents method to induce driver emotions
Studies emotional reactions of driver-passenger dyads
Develops a reliable emotion labeling process
Achieves expert consensus on emotion labels
Provides methodology for emotional understanding

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@INPROCEEDINGS{8373886, author={Alyuz, Nese and Aslan, Sinem and Healey, Jennifer and Alvarez, Ignacio J. and Esme, Asli Arslan}, booktitle={2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)}, title={Towards Understanding Emotional Reactions of Driver-Passenger Dyads in Automated Driving}, year={2018}, volume={}, number={}, pages={585-592}, keywords={Labeling;Vehicles;Data collection;Cameras;Automation;Roads;affective computing;automated driving;drive simulation;labeling;inter-rater agreement}, doi={10.1109/FG.2018.00093} }

Alvarez, I. (2018). . 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018).

Workshop on designing highly automated driving systems as radical innovation

Published in Adjunct Proceedings of the 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’18), ACM 2018

Abstract

Automated driving systems (ADS), especially in higher levels of automation, seem to be the new focus of innovation regarding future mobility. Technological achievements of traveling automation open up new challenges for road traffic. Existing automotive research focuses on problem solving and observational approaches including users and their imagination of the future of mobility to analyze acceptance and user experience of “incremental” (step-wised improved) innovations. On the other hand, “radical” (something new, enabled by technology or meaning change) innovations extensively increase product quality leaping over incremental innovation. This workshop aims to challenge the current research approaches to automated driving against “trying to improve sitting in a horse carriage” and discuss how we can design “radical” innovations for ADS beyond the “horse carriage”. Within this interactive workshop, we will utilize a design thinking approach to refocus on underlying problems that ADSs originally aim to solve and generate ideas for radical innovations.

Key Contributions

Critiques incremental innovation in ADS research
Promotes designing ADS as radical innovation
Applies a design thinking approach
Utilizes rapid prototyping for radical ideas
Refocuses on problems ADS should solve

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@inproceedings{10.1145/3239092.3239097, author = {Frison, Anna-Katharina and Riener, Andreas and Jeon, Myounghoon and Pfleging, Bastian and Alvarez, Ignacio}, title = {Workshop on Designing Highly Automated Driving Systems as Radical Innovation}, year = {2018}, isbn = {9781450359474}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3239092.3239097}, doi = {10.1145/3239092.3239097}, booktitle = {Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages = {37–43}, numpages = {7}, keywords = {Automated Driving Systems, Design Thinking, Innovation, SAE J3016, User-Centered Design}, location = {Toronto, ON, Canada}, series = {AutomotiveUI '18} }

Alvarez, I. (2018). . Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

2nd special session on solving the automated vehicle safety assurance challenge

Published in Proceedings of the 2019 International Conference on Intelligent Transportation Systems (ITSC), IEEE 2019

Abstract

The scientific knowledge and tools for the mass deployment of Automated Vehicles (AVs) are maturing rapidly, as evidenced by the wide deployment of AV test fleets worldwide. These vehicles have the potential to produce tremendous economic and societal benefits including greatly reduced traffic accidents, injuries, and congestion, and to make less expensive, more flexible and more productive transportation available to all. But one challenge looms above all others in the race to full vehicle automation; solving the AV Safety Assurance challenge. Automated transportation is not just a product, but an industry. And it is as an industry that we must together solve this challenge. We invite researchers, automakers, technology companies, and government regulators to come together to develop a holistic model to define and measure AV Safety. In this special session we present papers that provide contributions to the definition, applicability and standardization of AV safety assurance, including methods for the development of metrics, benchmarks and evangelisation of AV Safety Assurances to users and the public. This session continues the open discussion started in 2018 ITSC with the goal of making AV Safety a reality. The session will consist of high quality paper presentations as well as a panel discussion with some of the most relevant figures of the AV industry.

Key Contributions

Common AV safety definition
Fosters collaboration between industry and academia
Seeks to specify AV safety standards
Gathers a universal view on safety

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@inproceedings{elli20192nd, title={2nd special session on solving the automated vehicle safety assurance challenge}, author={Elli, Maria Soledad and Alvarez, Ignacio and Ota, Jeffrey and Weast, Jack and Maurer, Markus}, booktitle={International Conference on Intelligent Transportation Systems (ITSC)}, year={2019} }

Alvarez, I. (2019). . International Conference on Intelligent Transportation Systems (ITSC).

Design of a misbehavior detection system for objects based shared perception V2X applications

Published in ‘IEEE Intelligent Transportation Systems Conference’, IEEE 2019

Abstract

The recent trend of integrating vehicular communications with advanced sensors installed on vehicles, enables Connected and Autonomous Vehicles (CAVs) to share their own driving information as well as perception information, such as a list of perceived objects (e.g., dynamic obstacles such as vehicles, pedestrians, and cyclists, and static obstacles). This has the potential to improve driving safety by expanding collective perception of vehicles. However, adversaries may also populate false information to other Connected Vehicles (CVs) via Vehicle-to-Vehicle (V2V) communications. This paper investigates the security aspects of mixed deployment of CAVs, CVs and legacy vehicles, and in particular with regards to misbehavior detection. We provide a generic design framework that is independent from the specific algorithms of the underlying perception system, and can be used to implement a practical Misbehavior Detection System (MDS). We analyze the MDS framework w.r.t. a ghost vehicle attack. While no computing system can be completely secure, we believe this work would help the industry to develop a practical MDS design within a common framework while allowing individual techniques to mature and evolve over time with future academic research.

Key Contributions

Proposes a generic misbehavior detection framework
Divides detection into local/collaborative components
Categorizes four levels of anomaly detection
Defines Unobservability and Undecidability concepts
Defines conditions for successful attacks

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@inproceedings{8917066, author={Ambrosin, Moreno and Yang, Lily L and Liu, Xiruo and Sastry, Manoj R and Alvarez, Ignacio J}, booktitle={2019 IEEE Intelligent Transportation Systems Conference (ITSC)}, title={Design of a Misbehavior Detection System for Objects Based Shared Perception V2X Applications}, year={2019}, volume={}, number={}, pages={1165-1172}, keywords={Sensors;Autonomous vehicles;Roads;Safety;Space vehicles;Time measurement;Security}, doi={10.1109/ITSC.2019.8917066} }

Ambrosin, Moreno and Yang, Lily L and Liu, Xiruo and Sastry, Manoj R and Alvarez, Ignacio J (2019). Design of a misbehavior detection system for objects based shared perception V2X applications. 2019 IEEE Intelligent Transportation Systems Conference (ITSC).

Human-system cooperation in automated driving

Published in ‘International Journal of Human Computer Interaction’, Elsevier 2019

Abstract

To draw a comprehensive and cohesive understanding of human–vehicle cooperation in automated driving, a review is made on key studies in human–robot interaction and human factors. Throughout this article, insight is provided into how human drivers and vehicle systems interplay and influence each other. The limitations of technology-centered taxonomies of automation are discussed and the benefits of accounting for human agents are examined. The contributions of machine learning to automated driving and how critical models in human-system cooperation can inform the design of a more symbiotic relationship between driver and vehicle are investigated. Challenges in the human element to enable the safe introduction of road automation are also discussed. Particularly, the unintended consequences of vehicle automation on driver’s workload, situation awareness and trust are examined, and the social interactions between driver, vehicle, and other road users are investigated. This review will help professionals shape future directions for safer and more efficient and effective human–vehicle cooperation.

Key Contributions

Critiques technology-centered automation taxonomies
Promotes human-centered automation design.
Analyzes automation’s impact on drivers
Reviews machine learning for cooperation
Examines social human-vehicle interactions

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@article{biondi2019human, title={Human--vehicle cooperation in automated driving: A multidisciplinary review and appraisal}, author={Biondi, Francesco and Alvarez, Ignacio and Jeong, Kyeong-Ah}, journal={International Journal of Human--Computer Interaction}, volume={35}, number={11}, pages={932--946}, year={2019}, publisher={Taylor \& Francis} }

Biondi, Francesco and Alvarez, Ignacio and Jeong, Kyeong-Ah (2019). Human-system cooperation in automated driving. International Journal of Human--Computer Interaction.

Human–vehicle cooperation in automated driving: A multidisciplinary review and appraisal

Published in ‘International Journal of Human Computer Interaction’, Elsevier 2019

Abstract

To draw a comprehensive and cohesive understanding of human–vehicle cooperation in automated driving, a review is made on key studies in human–robot interaction and human factors. Throughout this article, insight is provided into how human drivers and vehicle systems interplay and influence each other. The limitations of technology-centered taxonomies of automation are discussed and the benefits of accounting for human agents are examined. The contributions of machine learning to automated driving and how critical models in human-system cooperation can inform the design of a more symbiotic relationship between driver and vehicle are investigated. Challenges in the human element to enable the safe introduction of road automation are also discussed. Particularly, the unintended consequences of vehicle automation on driver’s workload, situation awareness and trust are examined, and the social interactions between driver, vehicle, and other road users are investigated. This review will help professionals shape future directions for safer and more efficient and effective human–vehicle cooperation.

Key Contributions

Critiques technology-centered automation taxonomies
Promotes human-centered automation design.
Analyzes automation’s impact on drivers
Reviews machine learning for cooperation
Examines social human-vehicle interactions

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@article{biondi2019human, title={Human--vehicle cooperation in automated driving: A multidisciplinary review and appraisal}, author={Biondi, Francesco and Alvarez, Ignacio and Jeong, Kyeong-Ah}, journal={International Journal of Human--Computer Interaction}, volume={35}, number={11}, pages={932--946}, year={2019}, publisher={Taylor \& Francis} }

Biondi, Francesco and Alvarez, Ignacio and Jeong, Kyeong-Ah (2019). Human–vehicle cooperation in automated driving: A multidisciplinary review and appraisal. International Journal of Human--Computer Interaction.

Object-level perception sharing among connected vehicles

Published in Proceedings of 2019 IEEE Intelligent Transportation Systems Conference (ITSC), IEEE 2023

Abstract

Advances in vehicular communication technologies have made Connected Vehicles (CVs) a near-term reality. Compared to models where vehicles rely solely on their own perception to sense and make decision on the environment, CVs have the potential to allow information sharing among vehicles to improve sensing and decision making collectively. A first concrete step towards this goal is enabling vehicles to share perception-related information, to overcome limitations of their respective sensors (e.g., partial awareness due to occlusions). Sharing processed information vs raw sensor data has the advantage of reducing the amount of data to be transmitted, and the required computation burden at the receiver side.This paper proposes an approach to enable object-level sharing among vehicles. Following state-of-the-art object-level management techniques, we developed a two-layer architecture that handles object tracking and fusion from dynamic remote sources of information. We implemented our approach and showed that it can achieve realistic performance, and robustness both in terms of quality of information and computation.

Key Contributions

Proposes two-layer object fusion architecture
Defines connected vehicles as virtual sensors
Dynamically manages vehicles entering/leaving range
uses objects using distance-based clustering
Evaluates system accuracy and scalability

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@INPROCEEDINGS{8916837, author={Ambrosin, Moreno and Alvarez, Ignacio J and Buerkle, Cornelius and Yang, Lily L and Oboril, Fabian and Sastry, Manoj R and Sivanesan, Kathiravetpillai}, booktitle={2019 IEEE Intelligent Transportation Systems Conference (ITSC)}, title={Object-level Perception Sharing Among Connected Vehicles}, year={2019}, volume={}, number={}, pages={1566-1573}, keywords={Sensor fusion;Vehicle dynamics;Standards;Collaboration;Merging;Covariance matrices}, doi={10.1109/ITSC.2019.8916837} }

Alvarez, I. (2019). . 2019 IEEE Intelligent Transportation Systems Conference (ITSC).

The SKYNIVI experience: evoking startle and frustration in dyads and single drivers

Published in ‘Proceedings of the IEEE International Conference in Intelligent Transportations Systems 2019’, IEEE 2019

Abstract

To study naturalistic in-cabin emotion we developed SKYNIVI, a modified open source driving simulator, with scenarios designed to elicit startle and frustration. We target generating these emotions because we believe that by detecting these it will be possible for autonomous vehicles to learn to drive better. We show how to use SKYNIVI to develop datasets that capture naturalistic emotions in drivers and passengers for algorithmic development. We recruited 51 participants as dyads and single drivers to participate in two different scenarios. We show that we were able to evoke hundreds of instances of our target emotions in this cohort and present an analysis of factors we found to impact emotional expression including: scenario design , demographic factors, personality and baseline affect . We find that having a second person in the vehicle impacts observed expressions of emotion even when no difference in baseline affect is reported.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

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@INPROCEEDINGS{8813831, author={Alvarez, Ignacio and Healey, Jennifer and Lewis, Erica}, booktitle={2019 IEEE Intelligent Vehicles Symposium (IV)}, title={The SKYNIVI Experience: Evoking Startle and Frustration in Dyads and Single Drivers}, year={2019}, volume={}, number={}, pages={76-81}, keywords={Automobiles;Autonomous vehicles;Monitoring;Roads;Training}, doi={10.1109/IVS.2019.8813831} }

Alvarez, Ignacio and Healey, Jennifer and Lewis, Erica (2019). The SKYNIVI experience: evoking startle and frustration in dyads and single drivers. 2019 IEEE Intelligent Vehicles Symposium (IV).

Towards standardization of AV safety: C++ library for responsibility sensitive safety

Published in ‘Proceedings of the IEEE Intelligent Vehicle Symposium 2019’, IEEE 2019

Abstract

The need for safety in Automated Driving (AD) is becoming increasingly critical with the accelerating deployment of this technology. Beyond functional safety, industry must guarantee the operational safety of automated vehicles. Towards that end, Mobileye introduced the Responsibility Sensitive Safety (RSS), a model-based approach to Safety [1]. In this paper we expand upon this work introducing the C++ Library for Responsibility Sensitive Safety, an open source executable that implements a subset of RSS. We provide architectural details to integrate the C++ Library for Responsibility Sensitive Safety with AD Software pipelines as safety module overseeing decision making of driving policies. We illustrate this application with an example integration with the Baidu Apollo AD stack and simulator, [2] and [3], that provides safety validation of the planning module. Furthermore, we show how the C++ Library for Responsibility Sensitive Safety can be used to explore the usefulness of the RSS model through parameter exploration and analysis on minimum safe longitudinal distance, (dmin), considering different weather conditions. We also compare these results with half-of-speed rule followed in some parts of the world. We expect that the C++ Library for Responsibility Sensitive Safety becomes a critical component of future tools for formal verification, testing and validation of AD safety and that it helps bootstrap the AD research efforts towards standardization of safety.

Key Contributions

Safety analysis and validation
Standardized C++ library implementation
Modular software architecture design
Verification and validation methodology
Open-source safety model deployment

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Citation

@inproceedings{gassmann2019towards, title={Towards standardization of AV safety: C++ library for responsibility sensitive safety}, author={Gassmann, Bernd and Oboril, Fabian and Buerkle, Cornelius and Liu, Shuang and Yan, Shoumeng and Elli, Maria Soledad and Alvarez, Ignacio and Aerrabotu, Naveen and Jaber, Suhel and Van Beek, Peter and others}, booktitle={2019 IEEE Intelligent Vehicles Symposium (IV)}, pages={2265--2271}, year={2019}, organization={IEEE} }

Gassmann, Bernd and Oboril, Fabian and Buerkle, Cornelius and Liu, Shuang and Yan, Shoumeng and Elli, Maria Soledad and Alvarez, Ignacio and Aerrabotu, Naveen and Jaber, Suhel and Van Beek, Peter and others (2019). Towards standardization of AV safety: C++ library for responsibility sensitive safety. 2019 IEEE Intelligent Vehicles Symposium (IV).

Agents, environments, scenarios: A framework for examining models and simulations of human-vehicle interaction

Published in ‘Transportation research interdisciplinary perspectives’, 2020

Abstract

This paper provides a framework for examining human-vehicle interactions with respect to three dimensions that can involve models or simulations: the agents, the environments, and the scenarios. Agents are considered on a spectrum from human to artificial actors. Environments are considered on a spectrum from simulated to real. Scenarios are considered on a spectrum from constrained to unconstrained. It is argued that these three dimensions capture key differences in research approaches within the field of human-vehicle interaction, and that explicitly situating research and discussions within this framework will allow researchers to better compare and contrast research outcomes and contributions. The framework is used to locate different disciplines in the community with respect to one another, and to identify areas which are as-yet unexplored.

Key Contributions

Human-computer interaction design
Dimensions of simulation models
Automated Driving Development

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Citation

@article{janssen2020agents, title={Agents, environments, scenarios: A framework for examining models and simulations of human-vehicle interaction}, author={Janssen, Christian P and Boyle, Linda Ng and Ju, Wendy and Riener, Andreas and Alvarez, Ignacio}, journal={Transportation research interdisciplinary perspectives}, volume={8}, pages={100214}, year={2020}, publisher={Elsevier} }

Janssen, Christian P and Boyle, Linda Ng and Ju, Wendy and Riener, Andreas and Alvarez, Ignacio (2020). Agents, environments, scenarios: A framework for examining models and simulations of human-vehicle interaction. Transportation research interdisciplinary perspectives.

Auto-UI Global Perspectives

Published in ACM IX Interaction Journal, 2020

Abstract

ACM SIGCHI Auto-UI is a growing community, but one in which some continents were less involved than expected and hoped for. For the 2019 conference in Utrecht, the Netherlands, we made various targeted attempts to grow and diversify our international community, with support from the ACM SIGCHI Development Fund. Our efforts resulted in a growth in the number of Asian participants, which made up almost 20 percent of the attendees. In this blog, we briefly reflect on our initiatives and on a panel discussion focusing on research topics that matter more globally to the Auto-UI field.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

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Citation

@article{janssen2020auto, title={Auto-UI: global perspectives}, author={Janssen, Christian P and Schroeter, Ronald and Bidwell, Nicola J and Ji, Yong Gu and Alvarez, Ignacio and Bao, Shan and Jeon, Myounghoon and Boyle, Linda Ng and Donker, Stella F and Chuang, Lewis L and others}, journal={Interactions}, volume={27}, number={6}, pages={7--9}, year={2020}, publisher={ACM New York, NY, USA} }

Janssen, Christian P and Schroeter, Ronald and Bidwell, Nicola J and Ji, Yong Gu and Alvarez, Ignacio and Bao, Shan and Jeon, Myounghoon and Boyle, Linda Ng and Donker, Stella F and Chuang, Lewis L and others (2020). Auto-UI Global Perspectives. Interactions.

Emotion GaRage Vol. II A Workshop on Affective In-Vehicle Display Design

Published in ‘Proceedings of 12th International Conference in Automotive User Interfaces and Interactive Vehicular Applications’, 2020

Abstract

Driver performance and behavior can be partially predicated based on one’s emotional state. Through ascertaining the emotional state of passengers and employing various mitigation strategies, empathic cars can show potential in improving user experience and driving performance. Challenges remain in the implementation of such strategies, as individual differences play a large role in mediating the effect of affective intervention. Therefore, we propose a workshop that aims to bring together researchers and practitioners interested in affective interfaces and in-vehicle technologies as a forum for the development of targeted emotion intervention methods. During the workshop, we will focus on a common set of use cases and generate approaches that can suit different user groups. By the end of this short workshop, researchers will determine ideal intervention methods for prospective user groups. This will be achieved through the method of insight combination to generate and discuss ideas.

Key Contributions

Affective Computing in Automotive
Emotion-Aware HMI Design
Community Building

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Citation

@misc{nadri2020emotion, title={Emotion GaRage Vol. II}, author={Nadri, Chihab and Bosch, E and Oehl, M and Alvarez, I and Braun, M and Jeon, M}, year={2020}, publisher={II} }

Nadri, Chihab and Bosch, E and Oehl, M and Alvarez, I and Braun, M and Jeon, M (2020). Emotion GaRage Vol. II A Workshop on Affective In-Vehicle Display Design. .

Evaluation of responsibility-sensitive safety (rss) model based on human-in-the-loop driving simulation

Published in In proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, IEEE 2020.

Abstract

Safety is an important challenge in the development of automated vehicles (AVs). To help with the challenge of achieving higher safety in the decision making of AVs, Intel and Mobileye have proposed a parameterized model named Responsibility-Sensitive Safety (RSS). Previous studies have demonstrated that RSS has the potential to improve the safety performance of automated vehicles. However, RSS could lead to a considerable car-following distance depending on the parameter values chosen for the model, which could reduce traffic efficiency. To improve the efficiency of RSS applied to Adaptive Cruise Control (ACC) systems, previous work proposed an efficiency-optimal (referred as “Efficiency-optimal RSS”) variation of the RSS model that involves different triggering conditions of a proper response. Therefore, in this paper a human-in-the-loop driving simulation experiment was conducted to evaluate the performance and acceptance of different safety methods. The RSS model and the efficiencyoptimal variant were embedded in an ACC system based on Model Predictive Control (MPC) algorithm. Two car-following scenarios with a sudden deceleration of lead vehicle at various time headways were simulated to evaluate the human perception and response of the different models. Results show that the original RSS model improves subjective safety judgment of human drivers. While the Efficiency-optimal RSS variant has a lower subjective safety score when compared to original RSS, it significantly reduces driver’s emergency braking reactions when compare to an ACC only system.

Key Contributions

Human-in-the-loop evaluation framework
Comparative performance analysis (RSS vs. Human)
Safety model parameter sensitivity analysis
Empirical validation of a formal safety model
Simulation-based model calibration methodology

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Citation

@inproceedings{chai2020evaluation, title={Evaluation of responsibility-sensitive safety (rss) model based on human-in-the-loop driving simulation}, author={Chai, Chen and Zeng, Xianming and Alvarez, Ignacio and Elli, Maria Soledad}, booktitle={2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)}, pages={1--7}, year={2020}, organization={IEEE} }

Alvarez, I. (2020). . 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC).

How safe is safe enough? Automatic safety constraints boundary estimation for decision-making in automated vehicles

Published in Proceedings of IEEE Intelligent Vehicle Symposium 2020, IEEE 2020.

Abstract

The determination of safety assurances for automated driving vehicles is one of the most critical challenges in the industry today. Several behavioral safety models for automated driving have been proposed recently and standards discussions are on the way. In this paper we present a method to automatically explore the performance of automated vehicle (AV) safety models utilizing robustness of Metric Temporal Logic (MTL) specifications as a continuous metric of safety. We present a case study of the Responsibility Sensitive Safety model (RSS), introducing a safety evaluation pipeline based on the CARLA driving simulator, RSS and a set of safety-critical driving scenarios. Our method automatically extracts safety relevant profiles for these scenarios providing practical parametric boundaries for implementation. Furthermore, we evaluate the trade-offs between safety and utility within the safe RSS parameter space through a proposed naturalistic benchmark challenge that we open-sourced. We analyze different RSS parameter configurations including assertive and more conservative settings, extracted by our specification-driven framework. Our results show that while maintaining the safety boundaries, the extracted RSS configuration for assertive driving behavior achieves the highest utility.

Key Contributions

Safety boundary estimation framework
Parametric and non-parametric estimation methods
Simulation-based data generation and validation
Real-world data application and analysis
Systematic safety parameter definition

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Citation

@inproceedings{rodionova2020safe, title={How safe is safe enough? Automatic safety constraints boundary estimation for decision-making in automated vehicles}, author={Rodionova, Al{"e}na and Alvarez, Ignacio and Elli, Maria Soledad and Oboril, Fabian and Quast, Johannes and Mangharam, Rahul}, booktitle={2020 IEEE Intelligent Vehicles Symposium (IV)}, pages={1457--1464}, year={2020}, organization={IEEE} }

Alvarez, I. (2020). . 2020 IEEE Intelligent Vehicles Symposium (IV).

PURSS: Towards Perceptual Uncertainty Aware Responsibility Sensitive Safety with ML.

Published in ‘Proceedings of 2020 AAAI Conference’, AAAI 2020

Abstract

Automated driving is an ML-intensive problem and its safety depends on the integrity of perception as well as planning and control. Responsibility Sensitive Safety (RSS) is a recent approach to promote safe planning and control that relies on perfect perception; however, perceptual uncertainty is always present, and this causes the possibility of misperceptions that can lead an autonomous vehicle to allow unsafe actions. In this position paper, we sketch a novel proposal for a formal model of perception coupled with RSS to help mitigate the impact of misperception by using information about perceptual uncertainty. The approach expresses uncertainty as imprecise perceptions that are consumed by RSS and cause it to limit actions to those that support safe behaviour given the perceptual uncertainty. We illustrate our approach using examples and discuss its implications and limitations.

Key Contributions

Safety analysis and validation

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Citation

@inproceedings{salay2020purss, title={PURSS: Towards Perceptual Uncertainty Aware Responsibility Sensitive Safety with ML.}, author={Salay, Rick and Czarnecki, Krzysztof and Elli, Maria Soledad and Alvarez, Ignacio J and Sedwards, Sean and Weast, Jack}, booktitle={SafeAI@ AAAI}, pages={91--95}, year={2020}, organization={New York} }

Salay, Rick and Czarnecki, Krzysztof and Elli, Maria Soledad and Alvarez, Ignacio J and Sedwards, Sean and Weast, Jack (2020). PURSS: Towards Perceptual Uncertainty Aware Responsibility Sensitive Safety with ML.. SafeAI@ AAAI.

MISO-V: Misbehavior detection for collective perception services in vehicular communications

Published in ‘Proceedings of IEEE Intelligent Transportation Systems Conference 2021’, IEEE 2021

Abstract

Recently, Collective Perception Messages (CPM) that carry additional information about the surrounding environment beyond Basic Safety Messages (BSM) or Cooperative Awareness Messages (CAM) have been proposed to increase the situational awareness for Connected and Automated Vehicles (CAV) in Intelligent Transportation Systems. However, blindly trusting perception information from neighbors that cannot be locally verified is dangerous given the safety impact that erroneous or malicious information might have. This paper addresses the data trust challenge of CPMs, proposing a misbehavior detection scheme called MISO- V (Multiple Independent Sources of Observations over V2X) that leverages the inherently overlapping nature of the perception observations from multiple vehicles to verify the semantic correctness of the V2X data and improve the data trust and robustness of V2X systems. CPM-enabled CAVs are implemented and MISO-V performance is evaluated in CARLA-based simulation tool, where falsified V2X packets presenting a ghost car are injected in a suburban T-junction scenario with other cars. The results show that MISO- V is very effective in detecting the ghost car attacks and removing the impact of such misbehavior from influencing the receiver and offers a conservative and sensible approach towards trustworthy Collective Perception Services for CAVs.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

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Citation

@INPROCEEDINGS{9575970, author={Liu, Xiruo and Yang, Lily and Alvarez, Ignacio and Sivanesan, Kathiravetpillai and Merwaday, Arvind and Oboril, Fabian and Buerkle, Cornelius and Sastry, Manoj and Baltar, Leonardo Gomes}, booktitle={2021 IEEE Intelligent Vehicles Symposium (IV)}, title={MISO- V: Misbehavior Detection for Collective Perception Services in Vehicular Communications}, year={2021}, volume={}, number={}, pages={369-376}, keywords={Simulation;Semantics;Redundancy;Receivers;Tools;Robustness;Safety}, doi={10.1109/IV48863.2021.9575970} }

Liu, Xiruo and Yang, Lily and Alvarez, Ignacio and Sivanesan, Kathiravetpillai and Merwaday, Arvind and Oboril, Fabian and Buerkle, Cornelius and Sastry, Manoj and Baltar, Leonardo Gomes (2021). MISO-V: Misbehavior detection for collective perception services in vehicular communications. 2021 IEEE Intelligent Vehicles Symposium (IV).

On responsibility sensitive safety in car-following situations-a parameter analysis on german highways

Published in Proceedings of the IEEE Intelligent Vehicles Symposium (IV), IEEE 2021

Abstract

The need for safety in automated driving is undisputed. Since automated vehicles are expected to reduce the number of fatalities in road traffic significantly, hundreds of millions of test kilometers would be required for statistical safety validation [1]. Physics-based safety verification approaches are promising in order to reduce this validation effort. Towards this goal, Mobileye introduced the concept of Responsibility-Sensitive Safety (RSS). In RSS, bounds for the reasonable worst-case behavior of traffic participants are assumed to be given, such as the reaction time or the maximum deceleration. These parameters have a crucial effect on the applicability of the approach: choosing conservative parameters likely hinders traffic flow, while the opposite could lead to collisions, as the assumptions are violated. Thus, in this work, we focus on finding reasonable parameters of RSS. Based on the physical limits, legal requirements and human driving behavior, we propose scopes and parameter sets that allow for a sound safety verification while not hindering traffic flow. Furthermore, we present an approach that explains seemingly frequent human drivers’ RSS violations on highways and may lead to a useful extension of RSS.

Key Contributions

Safety analysis and validation
Automated driving system development
Verification and validation methods
Parameter Analysis

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Citation

@INPROCEEDINGS{9575420, author={Naumann, Maximilian and Wirth, Florian and Oboril, Fabian and Scholl, Kay–Ulrich and Elli, Maria Soledad and Alvarez, Ignacio and Weast, Jack and Stiller, Christoph}, booktitle={2021 IEEE Intelligent Vehicles Symposium (IV)}, title={On Responsibility Sensitive Safety in Car-following Situations - A Parameter Analysis on German Highways}, year={2021}, volume={}, number={}, pages={83-90}, keywords={Law;Intelligent vehicles;Roads;Safety;Brakes;Responsibility-Sensitive Safety (RSS);Automated Driving (AD);Safety Verification;Parameter Analysis}, doi={10.1109/IV48863.2021.9575420}}

Alvarez, I. (2021). . 2021 IEEE Intelligent Vehicles Symposium (IV).

To err is human: The role of human derived safety metrics in an age of automated vehicles

Published in ‘SAE Technical Paper’, SAE 2021

Abstract

As industry races to complete technical development of automated driving systems (ADS), important questions are being raised about how to measure the safety of such systems and the overall safety of Automated Vehicles (AVs). Traffic safety engineers have for decades utilized metrics to assess the safety of human drivers and measurements such as Time To Collision (TTC) and Time Headway (THW) have proven to be a useful indicator of increased risk of an accident for human drivers. But what if we can do better with AVs? Are human driving derived risk metrics meaningful for a self-driving vehicle? Recently, the Institute for Automated Mobility (IAM) published a set of metrics defined specifically for self-driving vehicles that provide a thorough assessment of the safety of an AV. While humans must use estimation and cautious judgement to make decisions, AVs can use precise measurement techniques via sensors and correlate multiple sources of data in real time. Utilizing information such as the reaction time of the ADS, the braking capability of the AV and more, the IAM proposed metrics allow for the assessment of the safety of an AV to be accurately measured, not as a notion of approximated risk, but as a binary calculation of safety. In this paper we analyze, compare and contrast human driving, risk-oriented safety metrics with the more definitive metrics proposed for AVs. We answer important questions about the necessary evolution of human derived metrics to ensure they are meaningful in the assessment of the safety of an AV, as well as whether novel metrics proposed for AVs can be used to better understand and assess the safety performance of AVs when compared to historical safety measures. Our research proves that AV-based assessment metrics can provide better insight into the safety of both AVs and human drivers.

Key Contributions

Safety analysis and validation
Automated driving system development

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Citation

@techreport{weast2021err, title={To err is human: The role of human derived safety metrics in an age of automated vehicles}, author={Weast, Jack and Elli, Maria and Alvarez, Ignacio}, year={2021}, institution={SAE Technical Paper} }

Weast, Jack and Elli, Maria and Alvarez, Ignacio (2021). To err is human: The role of human derived safety metrics in an age of automated vehicles. .

Driver emotions in automated vehicles

Abstract

There is little doubt that driving generates emotional responses, whether that’s the joy of freedom, the boredom of stop-and-go traffic or anger towards unsafe maneuvers. In this chapter we provide an overview of emotion research applied to the automotive context and highlight the impact of emotional states in varying levels of driving automation. We review the most critical research findings on the impact of emotional states in driving performance including reaction time and take-over readiness. We also discuss the application of emotion regulation strategies related to the driving task. Finally, we analyze the research challenges still present for robust emotional classification and personalization in their application to in-vehicle interactions. This technology offers great potential for the development of emotionally-aware in-cabin driver assistants which will play a critical role in the future of automated driving user experience development.

Key Contributions

Foundational theory compilation
UX design methods and frameworks
Interface application and case studies
Academia-industry knowledge transfer
Holistic user-centered design advocacy

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Citation

@incollection{fakhrhosseini2022driver, title={Driver emotions in automated vehicles}, author={FakhrHosseini, Shabnam and Ko, Sangjin and Alvarez, Ignacio and Jeon, Myounghoon}, booktitle={User Experience Design in the Era of Automated Driving}, pages={85--97}, year={2022}, publisher={Springer} }

Alvarez, I. (2022). . User Experience Design in the Era of Automated Driving.

Emotion GaRage Vol. III: A Workshop on Affective In-Vehicle Display Applications

Published in ‘Adjunct Proceedings of the 14th International Conference on Automotive User Interfaces and Interactive Vehicular Applications’, ACM 2022

Abstract

Empathic in-vehicle interfaces can address driver affect and mitigate decreases in driving performance and behavior that are associated with emotional states. Empathic vehicles can detect and employ a variety of intervention modalities to change user affect and improve user experience. Challenges remain in the implementation of such strategies, as a broader established view of practical intervention modalities and strategies is still absent. Therefore, we propose a workshop that aims to bring together researchers and practitioners interested in affective interfaces and in-vehicle technologies as a forum for the development of displays and alternatives suitable to various use case situations in current and future vehicle states. During the workshop, we will focus on a common set of use cases and generate approaches that can suit different user groups. By the end of this workshop, researchers will create a design flowchart for in-vehicle affective display designers when creating displays for an empathic vehicle.

Key Contributions

HCI Interaction Design
Emotional and affective computing
HCI Prototyping
Workshop organization and community building

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Citation

@inproceedings{10.1145/3544999.3550161, author = {Nadri, Chihab and Dong, Jiayuan and Li, Jingyi and Alvarez, Ignacio and Jeon, Myounghoon}, title = {Emotion GaRage Vol. III: A Workshop on Affective In-Vehicle Display Applications}, year = {2022}, isbn = {9781450394284}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3544999.3550161}, doi = {10.1145/3544999.3550161}, pages = {186–188}, numpages = {3}, keywords = {Empathic vehicles, affective computing, emotions, human-centered computing, interaction design, user experience}, location = {Seoul, Republic of Korea}, series = {AutomotiveUI '22} }

Nadri, Chihab and Dong, Jiayuan and Li, Jingyi and Alvarez, Ignacio and Jeon, Myounghoon (2022). Emotion GaRage Vol. III: A Workshop on Affective In-Vehicle Display Applications. .

Empathic vehicle design: Use cases and design directions from two workshops

Published in ‘Proceedings of ACM’s Computer Human Interaction Conference (CHI)’, ACM 2022

Abstract

Empathic vehicles are expected to improve user experience in automated vehicles and to help increase user acceptance of technology. However, little is known about potential real-world implementations and designs using empathic interfaces in vehicles with higher levels of automation. Given advances in affect detection and emotion mitigation, we conducted two workshops (N1 =24, N2 = 22, Ntotal = 46) on the design of empathic vehicles and their potential utility in a variety of applications. This paper recapitulates key opportunities in the design and application of empathetic interfaces in automated vehicles which emerged from the two workshops hosted at the ACM AutoUI conferences.

Key Contributions

Emphathetic HMI design in Automotive
Generative AI prototyping
Workshop organization and community building

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Citation

@inproceedings{nadri2022empathic, title={Empathic vehicle design: Use cases and design directions from two workshops}, author={Nadri, Chihab and Alvarez, Ignacio and Bosch, Esther and Oehl, Michael and Braun, Michael and Healey, Jennifer and Jallais, Christophe and Ju, Wendy and Li, Jingyi and Jeon, Myounghoon}, booktitle={CHI Conference on Human Factors in Computing Systems Extended Abstracts}, pages={1--7}, year={2022} }

Nadri, Chihab and Alvarez, Ignacio and Bosch, Esther and Oehl, Michael and Braun, Michael and Healey, Jennifer and Jallais, Christophe and Ju, Wendy and Li, Jingyi and Jeon, Myounghoon (2022). Empathic vehicle design: Use cases and design directions from two workshops. CHI Conference on Human Factors in Computing Systems Extended Abstracts.

IEEE Standard for Assumptions in Safety-Related Models for Automated Driving Systems

Abstract

This standard applies to road vehicles. It defines a minimum set of reasonable assumptions and foreseeable scenarios that shall be considered in the development of safety related models that are part of an automated driving system (ADS). Scope: This standard applies to road vehicles. For a set of scenarios, a minimum set of assumptions regarding reasonably foreseeable behaviors of other road users are defined that shall be considered in the development of safety-related models for automated driving systems (ADS). This standard further defines a list of attributes common to contributed safety-related models and methods to help verify whether a safety-related model takes the minimum set of assumptions into consideration. An informative annex instantiates several examples of how the proposed minimum set of assumptions could be employed in ADS development. Sources of uncertainty, such as prediction or perception errors, are out of scope to this standard. This standard does not guarantee the safety of the overall system in all scenarios. Purpose: Government and Industry alike need an open, transparent, and technology-neutral standard that provides guidance useful for evaluating the performance of an ADS. This guidance consists of a minimum set of assumptions with bounds on reasonably foreseeable behaviors of other road users used in the development of safety-related models.

Key Contributions

Minimum assumption definition for ADAS
Reasonable Foreseeable scenario specification
Technology-neutral evaluation guidance
Model verification methodology

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BibTeX

@ARTICLE{9761121, author={}, journal={IEEE Std 2846-2022}, title={IEEE Standard for Assumptions in Safety-Related Models for Automated Driving Systems}, year={2022}, volume={}, number={}, pages={1-59}, keywords={IEEE Standards;Autonomous vehicles;Vehicle safety;Autonomous driving;Vehicle driving;Data privacy;Decision making;ADS;ADS-operated vehicle;automated driving system;assumption;automated vehicle;autonomous vehicles;AV;decision-making;IEEE 2846}, doi={10.1109/IEEESTD.2022.9761121}}

Quantifying Error Propagation in Multi-Stage Perception System of Autonomous Vehicles via Physics-Based Simulation

Published in Proceedings of the IEEE 2022 Winter Simulation Conference, IEEE 2023

Abstract

Ensuring the safety of autonomous vehicle (AV) relies on accurate prediction of error occurrences in its perception system. Due to the inter-stage functional dependence, the error occurred at a certain stage may be propagated to the following stage and generate extra errors. To quantify the error propagation, this paper adopts the physics-based simulation, which enables fault injection at different stages of an AV perception system to generate error event data for error propagation modeling. A multi -stage Hawkes process (MSHP) is proposed to predict the error occurrences in each stage, with error propagation represented as a latent triggering mechanism. With explicitly considering the error propagation mechanism, the proposed outperforms benchmark methods in predicting error occurrence in a physics-based simulation of a multistage AV perception system. The proposed two-step likelihood-based algorithm accurately estimates the model coefficients in a numerical simulation case study.

Key Contributions

Integrated simulation and modeling framework
Fault-injection simulation for data generation
Statistical error propagation modeling
Model parameter estimation algorithm
Model validation and performance benchmarking

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Citation

@inproceedings{pan2022quantifying, title={Quantifying error propagation in multi-stage perception system of autonomous vehicles via physics-based simulation}, author={Pan, Fenglian and Zhang, Yinwei and Head, Larry and Liu, Jian and Elli, Maria and Alvarez, Ignacio}, booktitle={2022 Winter Simulation Conference (WSC)}, pages={2511--2522}, year={2022}, organization={IEEE} }

Alvarez, I. (2022). . 2022 Winter Simulation Conference (WSC).

Special Issue

Published in ‘MDPI Sensors’, 2022

Abstract

Connected and automated driving technologies have the potential to revolutionize transportation by facilitating mobility services to a wider population, improving safety and traffic efficiency. Automated driving technology is expected to reduce the number of accidents caused by human error and avert deadly crashes, ensure mobility for all, including old and impaired individuals, allow the human driver to perform alternative (secondary) tasks, increase traffic flow efficiency, reduce fuel consumption, and lower emissions.

Driven by these goals, humankind is experiencing an exponential growth in vehicle automation taking over the monitoring of surroundings and vehicle control tasks from human drivers in a quest towards full autonomy. Connected and automated vehicles are equipped with multimodal sensors that allow continuous perception and monitoring of driving tasks to assist drivers in lower levels of SAE automation or to fully take control of driving tasks under full SAE automation. Numerous sensors, both inside and outside vehicles, allow the detection and identification of oncoming obstacles, the determination of their velocity, and the prediction of future behaviours to avoid potential collisions. Each sensor has its own strengths and weaknesses in terms of range, accuracy, energy consumption. and sensitivity towards external conditions such as weather and light. Automated vehicles usually rely on a mix of signals to improve operational reliability and robustness under the dynamic external conditions of real-world deployments. Generally, we can divide external AV sensors into two major groups: active and passive. Active sensors generate an active signal (electromagnetic or light) transmitted to the external environment to analyse its reflection (e.g., radar, lidar), whereas passive sensors just record the information from the environment (e.g., camera). Additionally, there have been advances in intelligent transportation infrastructure to monitor road users, perform predictive analytics, and facilitate collaborative perception services and remote vehicle control.

The increasing commercial availability of conditional automation (SAE level 3) and the incoming Robotaxi services (SAE Level 4) have also resulted in an increase in in-cabin monitoring sensors dedicated to monitoring driver and passenger behaviours. Multimodal in-cabin monitoring systems are crucial enablers for successfully managing automated vehicle operations. These systems enable the detection of the driver/passenger’s physiological state and activity to assess their readiness to take over control of the vehicle if required as well as to monitor their safety. Driving monitoring solutions provide information on occupants’ fatigue, distraction, discomfort, and stress. Furthermore, they can help to verify that automation is used properly by evaluating engagement in the driving monitoring task or the inherent risk of the non-driving tasks.

This Special issue aims to collect original theoretical or empirical articles on different sensing technologies, solutions, and applications for automated vehicles.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

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Citation

@article{SpecialIssue, author = {Ignacio Alvarez, Jaka Sodnik and Nikolas Thomopoulos}, title = {Robust Multimodal Sensing for Automated Driving Systems}, journal = {Sensors}, year = {2022}, volume = {22}, issue = {13}, pages = {}, doi = {ISSN 1424-8220} }

Ignacio Alvarez, Jaka Sodnik and Nikolas Thomopoulos (2022). Special Issue. Sensors.

User experience design in the era of automated driving

Published in Journal Name, 2023

Abstract

This book is dedicated to user experience design for automated driving to address humane aspects of automated driving, e.g., workload, safety, trust, ethics, and acceptance. Automated driving has experienced a major development boost in recent years. However, most of the research and implementation has been technology-driven, rather than human-centered. The levels of automated driving have been poorly defined and inconsistently used. A variety of application scenarios and restrictions has been ambiguous. Also, it deals with human factors, design practices and methods, as well as applications, such as multimodal infotainment, virtual reality, augmented reality, and interactions in and outside users. This book aims at 1) providing engineers, designers, and practitioners with a broad overview of the state-of-the-art user experience research in automated driving to speed-up the implementation of automated vehicles and 2) helping researchers and students benefit from various perspectives and approaches to generate new research ideas and conduct more integrated research.

Key Contributions

State-of-the-art UX Design in Automated Driving
Foundational theory compilation
UX design methods and frameworks
Interface application and case studies

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Citation

@book{riener2022user, title={User experience design in the era of automated driving}, author={Riener, Andreas and Jeon, Myounghoon and Alvarez, Ignacio}, volume={980}, year={2022}, publisher={Springer} }

Alvarez, I. (2022). . .

White Paper-Literature Review on Kinematic Properties of Road Users for Use on Safety-Related Models for Automated Driving Systems

Published in IEEE Standards, 2022

Abstract

This document presents a review of relevant literature (e.g., standards, regulations, and scientific publications) that investigated kinematic behavior of road users. This review is intended to serve as a key contribution to the Automated Driving Systems (ADS) research and industry communities, as well as to current standardization efforts, such as IEEE Std 2846, IEEE Standard for Assumptions in Safety-Related Models for Automated Driving Systems.

Key Contributions

Literature review road actors kinematic behavior
Systematic data organization of kinematic parameter values
Kinematic data contextualization
Research gap analysis

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Citation

@ARTICLE{9763462, author={IEEE VT, ITS}, journal={Literature Review on Kinematic Properties of Road Users for Use on Safety-Related Models for Automated Driving Systems}, title={Literature Review on Kinematic Properties of Road Users for Use on Safety-Related Models for Automated Driving Systems}, year={2022}, volume={}, number={}, pages={1-35}, keywords={ADS;automated driving systems;behaviors;IEEE 2846;kinematic;literature review;road users;standards;white paper}, doi={} }

Alvarez, I. (2022). . Literature Review on Kinematic Properties of Road Users for Use on Safety-Related Models for Automated Driving Systems.

Application of Responsibility-Sensitive Safety in Areas with Limited Visibility: Occlusions in RSS

Published in ‘2023 IEEE 26th International Conference on Intelligent Transportation Systems’, IEEE 2023

Abstract

Areas of limited visibility are common in day-to-day traffic: be it static buildings, parked vehicles, traffic participants blocking the vehicle’s line of sight, harsh weather conditions or just narrow curves that impede the automated driving sensor suite to inspect the road ahead. Autonomous vehicles have to be able to safely cope with this kind of constraints. The Responsibility-Sensitive Safety model (RSS) demands vehicles to exercise caution with respect to occlusions and to consider also occluded road agents. This paper provides a concrete implementation of how occlusions in RSS can be addressed and investigates the balance between safety and usefulness of the model when a reasonably foreseeable behavior of occluded road agents is assumed. We perform occlusion experiments in urban as well as on highway scenarios with the driving simulation platform CARLA applying different parameterization of the agents kinematic properties and the safety model parameters to analyse and judge the consequences with respect to safe driving and overcautious driving behaviors.

Key Contributions

Safety analysis and validation
Implements occlusion handling with artificial vehicles
Evaluates approach in urban/highway scenarios
Shows aggressive parameters can reduce usability
Analyzes real-world data for assumptions
Demonstrates collision mitigation via simulation

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Citation

@INPROCEEDINGS{10421912, author={Gassmann, Bernd and Dey, Shreya and Alvarez, Ignacio and Oboril, Fabian and Scholl, Kay-Ulrich}, booktitle={2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)}, title={Application of Responsibility-Sensitive Safety in Areas with Limited Visibility: Occlusions in RSS}, year={2023}, volume={}, number={}, pages={5976-5981}, keywords={Roads;Kinematics;Safety;Behavioral sciences;Usability;Intelligent transportation systems;Meteorology}, doi={10.1109/ITSC57777.2023.10421912} }

Gassmann, Bernd and Dey, Shreya and Alvarez, Ignacio and Oboril, Fabian and Scholl, Kay-Ulrich (2023). Application of Responsibility-Sensitive Safety in Areas with Limited Visibility: Occlusions in RSS. 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC).

Emotion GaRage Vol. IV: Creating empathic in-vehicle interfaces with generative AIs for automated vehicle contexts

Published in ‘Proceedings of ACM’s Automotive UI 2023’, ACM 2023

Abstract

This workshop aims to design advanced empathic user interfaces for in-vehicle displays, particularly for high-level automated vehicles (SAE level 3 or higher). Incorporating model-based approaches for understanding human emotion regulation, it seeks to enhance the user-vehicle interaction. A unique aspect of this workshop is the integration of generative artificial intelligence (AI) tools in the design process. The workshop will explore generative AI’s potential in crafting contextual responses and its impact on user experience and interface design. The agenda includes brainstorming on various driving scenarios, developing emotion-oriented intervention methods, and rapid prototyping with AI tools. The anticipated outcome includes practical prototypes of affective user interfaces and insights on the role of AI in designing human-machine interactions. Through this workshop, we hope to contribute to making automated driving more accessible and enjoyable.

Key Contributions

Automated driving system development
Emotional and affective computing
Designs advanced empathic user interfaces
Integrates generative AI in design process
Applies model-based emotion regulation methods

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Citation

@inproceedings{10.1145/3581961.3609828, author = {Choe, Mungyeong and Bosch, Esther and Dong, Jiayuan and Alvarez, Ignacio and Oehl, Michael and Jallais, Christophe and Alsaid, Areen and Nadri, Chihab and Jeon, Myounghoon}, title = {Emotion GaRage Vol. IV: Creating Empathic In-Vehicle Interfaces with Generative AIs for Automated Vehicle Contexts}, year = {2023}, isbn = {9798400701122}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3581961.3609828}, doi = {10.1145/3581961.3609828}, booktitle = {Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages = {234–236}, numpages = {3}, keywords = {ChatGPT, affective computing, emotions, empathic vehicles, interaction design}, location = {Ingolstadt, Germany}, series = {AutomotiveUI '23 Adjunct} }

Choe, Mungyeong and Bosch, Esther and Dong, Jiayuan and Alvarez, Ignacio and Oehl, Michael and Jallais, Christophe and Alsaid, Areen and Nadri, Chihab and Jeon, Myounghoon (2023). Emotion GaRage Vol. IV: Creating empathic in-vehicle interfaces with generative AIs for automated vehicle contexts. Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

“Play Your Anger”: A report on the empathic in-vehicle interface workshop

Published in ‘Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications’, 2023

Abstract

Empathic in-vehicle interfaces are critical in improving user safety and experiences. There has been much research on how to estimate drivers’ affective states, whereas little research has investigated intervention methods that mitigate potential impacts from the driver’s affective states on their driving performance and user experiences. To enhance the development of in-vehicle interfaces considering emotional aspects, we have organized a workshop series to gather automotive user interface experts to discuss this topic at the International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI). The present paper focuses particularly on the intervention methods created by the experts and proposes design recommendations for future empathic in-vehicle interfaces. We hope this work can spark lively discussions on the importance of drivers’ affective states in their user experience of automated vehicles and pose the right direction.

Key Contributions

Presents novel empathic interface prototypes.
Offers expert-driven design recommendations.
Addresses anger, frustration, and fear.
Details a three-phase design process.
Focuses on specific intervention methods.

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Citation

@inproceedings{10.1145/3581961.3609865, author = {Dong, Jiayuan and Nadri, Chihab and Alvarez, Ignacio and Diels, Cyriel and Lee, Myeongkyu and Li, Jingyi and Liao, Pei Hsuan and Manger, Carina and Sadeghian, Shadan and Schu\ss{}, Martina and Walker, Bruce N. and Walker, Francesco and Wang, Yiyuan and Jeon, Myounghoon}, title = {“Play Your Anger”: A Report on the Empathic In-vehicle Interface Workshop}, year = {2023}, isbn = {9798400701122}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3581961.3609865}, doi = {10.1145/3581961.3609865}, booktitle = {Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages = {168–173}, numpages = {6}, location = {Ingolstadt, Germany}, series = {AutomotiveUI '23 Adjunct} }

Dong, Jiayuan and Nadri, Chihab and Alvarez, Ignacio and Diels, Cyriel and Lee, Myeongkyu and Li, Jingyi and Liao, Pei Hsuan and Manger, Carina and Sadeghian, Shadan and Schu\ss{ (2023). “Play Your Anger”: A report on the empathic in-vehicle interface workshop. Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

RSS Demonstrator: a Tool for User Experience Interactions with Automated Driving Safety Models

Published in ‘Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications’, 2023

Abstract

Safety Assurance remains a challenge for the large-scale deployment of Automated Driving Systems (ADS). Safety models monitor the performance of the ADS. Most safety models are validated both in simulation and during on-road tests. However, first-hand experiences and analysis of ADS safety models are not easily accessible to the general research community. This paper introduces the RSS driving demonstrator an open-source simulation tool that enables first-hand experience of the Responsibility Sensitive Safety (RSS) safety model proposed by Intel and Mobileye and adopted by several Automotive Industry standards and regulatory frameworks. The RSS demonstrator enables first-hand interactions and experience of ADS safety model restrictions in both automated and manual driving conditions. As a User Experience (UX) tool, it provides quantitative safety metrics and flexible user interaction features. The results indicate it served to both evangelize the RSS ADS safety model with laymen population and is a versatile tool for Automotive UX development.

Key Contributions

Safety analysis and validation
User experience design and evaluation
Automated driving system development
Tool development and demonstration
Human-computer interaction design

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Citation

@inproceedings{10.1145/3581961.3609894, author = {Alvarez, Ignacio and Gassmann, Bernd and Pasch, Frederik and Oboril, Fabian}, title = {RSS Demonstrator: a Tool for User Experience Interactions with Automated Driving Safety Models}, year = {2023}, isbn = {9798400701122}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3581961.3609894}, doi = {10.1145/3581961.3609894}, pages = {180–185}, numpages = {6}, keywords = {Automated Driving Safety Model, Automated Driving System, Driving Simulation, RSS, Safety, User Experience}, location = {Ingolstadt, Germany}, series = {AutomotiveUI '23 Adjunct} }

Alvarez, Ignacio and Gassmann, Bernd and Pasch, Frederik and Oboril, Fabian (2023). RSS Demonstrator: a Tool for User Experience Interactions with Automated Driving Safety Models. .

Inside Out: Emotion GaRage Vol. V

Published in ‘Adjunct Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications’, 2024

Abstract

The rapid advancement of automated vehicles has aroused the curiosity of researchers in the automotive field. Understanding the emotional aspects of this technology is critical to improving human-vehicle interactions. The topics of the proposed workshop will be expanded from internal to external empathetic interface designs of automated vehicles. The workshop will gather researchers and practitioners to brainstorm and design affective internal and external interfaces for automated vehicles, targeting specific use cases within the social context. During the workshop, participants will use an affective design tool and generative AI to prototype affective interface designs in automated vehicles. With this creative approach, we aim to expand the knowledge of affective eHMIs in addition to in-vehicle designs and understand social factors that contribute to the user perceptions of automated vehicles.

Key Contributions

Emotional and affective computing
Expands focus to external vehicle interfaces
Aims to create design guidelines
Addresses social context and ethical issues
Prototype affective interface designs

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Citation

@inproceedings{10.1145/3641308.3677403, author = {Dong, Jiayuan and Gowda, Nikhil and Wang, Yiyuan and Choe, Mungyeong and Alsaid, Areen and Alvarez, Ignacio and Krome, Sven and Jeon, Myounghoon}, title = {Inside Out: Emotion GaRage Vol. V}, year = {2024}, isbn = {9798400705205}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3641308.3677403}, doi = {10.1145/3641308.3677403}, pages = {260–263}, numpages = {4}, keywords = {affective external human-machine interaction designs, emotions, empathic in-vehicle interfaces, generative artificial intelligence}, location = {Stanford, CA, USA}, series = {AutomotiveUI '24 Adjunct} }

Dong, Jiayuan and Gowda, Nikhil and Wang, Yiyuan and Choe, Mungyeong and Alsaid, Areen and Alvarez, Ignacio and Krome, Sven and Jeon, Myounghoon (2024). Inside Out: Emotion GaRage Vol. V. .

Reliability modeling for perception systems in autonomous vehicles: A recursive event-triggering point process approach

Published in ‘Transportation Research Part C: Emerging Technologies’, 2024

Abstract

Ensuring the reliability of sensor-fusion-based perception systems is crucial for the safe deployment of autonomous vehicles. Such systems function through a sequence of interconnected stages, where errors in upstream stages may propagate to downstream stages and trigger additional errors. The cross-stage error propagation conceptually exists and makes errors in different stages, not independent, posing model challenges, estimation challenges, and data challenges for reliability modeling. The existing methods cannot be applied to address all these challenges. Thus, this paper presents a recursive event-triggering point process to explicitly consider the error propagation based on the simulated data. The data are simulated from a proposed error injection framework, which can generate various errors from a sequence of interconnected stages in a perception system. The latent and probabilistic error propagation information is incorporated into a modified expectation–maximization (EM) algorithm for parameter estimation. The numerical and physics-based simulation case studies demonstrate the prediction accuracy and interpretability of the proposed modeling methodology.

Key Contributions

Automated driving system development
Proposes recursive event-triggering point process
Develops modified EM estimation algorithm
Presents simulation error injection framework
Differentiates primary and propagated errors
Models redundant multi-module perception systems

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Citation

@article{pan2024reliability, title={Reliability modeling for perception systems in autonomous vehicles: A recursive event-triggering point process approach}, author={Pan, Fenglian and Zhang, Yinwei and Liu, Jian and Head, Larry and Elli, Maria and Alvarez, Ignacio}, journal={Transportation Research Part C: Emerging Technologies}, volume={169}, pages={104868}, year={2024}, publisher={Elsevier} }

Pan, Fenglian and Zhang, Yinwei and Liu, Jian and Head, Larry and Elli, Maria and Alvarez, Ignacio (2024). Reliability modeling for perception systems in autonomous vehicles: A recursive event-triggering point process approach. Transportation Research Part C: Emerging Technologies.

talks

Challenges and Opportunities in Automated Driving in the Era of Agentic AI

I recently had the pleasure of speaking to the students of the Máster de Movilidad Cooperativa Conectada y Autónoma at Universidad Carlos III de Madrid, sharing how Generative AI is reshaping every corner of automated‑driving R&D. My session, “Challenges and Opportunities in Automated Driving in the Era of Agentic AI,” argued that these new AI models are far more than hype, they mark a genuine shift in the way we design, test, and ultimately will trust autonomous vehicles. Here are a few highlights from the talk content.

Generative AI Landscape

I started by defining what Generative AI means to me. I find that these terms are often overused, and end up meaning different things to different people. In my mind, Generative AI is a subset of Deep Learning, a group of algorithms that create new data rather than merely classify existing inputs. Whether through GANs, VAEs, diffusion pipelines, Transformers, or state‑space networks, these algorithms learn rich distributions of data representations from enormous data corpora and when trained, they become foundation models such as GPT‑n. Unlike hand‑coded software, they’re trained statistical models that approximate facets of human cognition. Based on the type of data they were trained we will have multiple “flavors” of foundation models with different capabilities.

So LLMs are not computer programs, they are “models”. Here is an analogy I like: if a computer program is like a recipe with precise steps, a model, is like having a chef who has learned to cook by tasting countless dishes and understanding flavor combinations. You can ask this “chef” to create new dishes based on this data-driven (learned) understanding, even if they haven’t seen that exact recipe before.

But LLMs are made of code. The “program” in this case is the architecture of the neural network and the learning algorithm, while the “model” is the resulting set of learned weights and biases within that network.

Generative AI cook

After getting our basis, I turned to the autonomous‑vehicle (AV) pipeline itself, where applications today cover the complete AV development stack. From simulation for realistic synthetic world generation as well as verification and validation, to perception problems, creating maps, predicting road user behaviors and new conversational AI assistants for passengers.

On simulation, I touched on solutions like Scenario Diffusion from Zoox that conjure complex, multi‑agent traffic scenes conditioned on HD maps and textual prompts, or UniSim by Waabi, which transforms mundane driving logs into editable, photorealistic worlds. These synthetic environments slash the expense and risk of road testing while letting engineers probe rare edge cases at scale.

On perception, I showcased DatasetGAN, an NVIDIA technique that simplifies manual labeling tasks and wiht a handful of labeled images spanws vast troves of perfectly segmented data.

One of the highlights on planning applicaitons is BehaviorGPT that impressed everyone with its ability to predict myriad plausible trajectories for every road user, capturing the uncertainty of traffic.

And as we get closer to the frontier of end‑to‑end decision making. DriveGPT4 shows how fused video perception with language‑model reasoning, can output low‑level control commands and natural‑language justifications for human consumption.

GenAI Pipeline

Of course, no talk is complete without a reality check. I emphasized the twin troubles of scarce, high‑quality multimodal data and the scary computational appetite of today’s large models. Achieving sub‑100‑millisecond latency for LLMs on in‑car hardware is no trivial feat. Security and safety are very real threats (think prompt‑injection attacks, IP leakage, nondeterministic outputs, and opaque reasoning) all of these challenge regulators and engineers alike.

Yet the opportunities are equally compelling. Model‑compression tricks like quantization, pruning, and distillation can turn bloated networks into nimble binaries, while neuromorphic chips such as Intel’s Loihi handle spike‑based inference with astonishing energy efficiency. Safety, too, can be engineered: binding language‑model suggestions inside a rigorously proven shell like Mobileye’s RSS keeps behaviors within an acceptable envelope, and tools like LVLM Interpret begin to open the model’s “black box,” offering visibility into why a recommendation was made.

Generative AI Landscape

I closed by reminding people that the march towards full autonomy is a marathon, but Generative AI has armed us with fresh answers to old questions. If we address data, performance, security, and safety head‑on, we can unlock a future of safer, more efficient, and more intelligent mobility. The technology is here to stay; our task now is to wield it ethically, safely, and wisely.

Thanks for reading! Contact me if you think the full content of this talk would be of interest to you or and audience of your choice.

—Prof. Ignacio Alvarez

Future of AI Transport

teaching

Study design and implementation of UX tests: AV harrassment

Course Icon

Description

This course is designed to prepare students for their final thesis. It provides fundamental knowledge to develop a study design based on a problem definition, conduct the study, evaluate it, and interpret the results. Thematic clusters in the area of human-computer interaction (e.g., productivity, automated driving, sports, and digitalization) are offered, from which student groups can choose and propose a specific topic. This topic will be developed iteratively in close professional coordination with the respective supervising lecturers. • Basics of user studies (possible applications, definition of research hypothesis) • Study design (dependent/independent variables, laboratory vs. field studies, within-groups/between-groups design) • Planning of experiments (different methods, “fidelity” of an experiment, software/hardware prototypes, Wizard of Oz studies, qualitative surveys/quantitative measurements, ethical aspects, role of an institutional review board (IRB)) • Study implementation (preparation, briefing/debriefing, finding and inviting test subjects, determining group size, learning effects, measuring variables/data collection) • Qualitative data analysis (content analysis, evaluation with MAXQDA/NVIVO, preparation and presentation of results, revision/fine-tuning in Illustrator) • Quantitative data analysis (reflection on the research hypothesis, use of SPSS for statistical evaluations, parametric/nonparametric statistics, correct choice of method, presentation and interpretation of results) • Written/oral presentation of the results (preparation of results, peer review process, final conference-style presentation)

Exploring and Addressing Aggression, Vandalism, Harassment and Abuse of Autonomous Robots

Recent developments highlight the growing role of automated systems in urban mobility. For example, autonomous delivery robots (ADRs), which facilitate contactless delivery, were increasingly used to support lastmile logistics during the COVID-19 pandemic. Additionally, Waymo’s robotaxis have recently completed 150,000 commercial rides and cover approximately 1.6 million kilometers per week. While these developments offer promising mobility solutions, ensuring broad public acceptance is crucial. One of the challenges currently faced is the risk of vandalism, harassment, and aggression against these systems, which can significantly impact safety, usability, and public trust. Addressing these concerns requires a proactive design approach that mitigates risks and enhances security. Within this seminar, students analyzed aspects of aggression, vandalism, harassment, and abuse directed at autonomous robots (e.g. delivery service robots, automated vehicles) with the goal to develop interfaces and technological solutions that mitigate these risks while ensuring the vehicles remain safe and functional for all users. Based on an individual literature review (related work), students (in groups of two or three) identified key challenges and explored innovative concepts. be developed together in a dedicated brainstorming session and refined/substantiated by the individual teams (with feedback from the lecturer).

Student Project Details

Coming soon

Interested?

Contact me if you’d like me to teach this course to you or your audience.

Study design and implementation of UX tests: Emphatic Car

Course Icon

Description

This course is designed to prepare students for their final thesis. It provides fundamental knowledge to develop a study design based on a problem definition, conduct the study, evaluate it, and interpret the results. Thematic clusters in the area of human-computer interaction (e.g., productivity, automated driving, sports, and digitalization) are offered, from which student groups can choose and propose a specific topic. This topic will be developed iteratively in close professional coordination with the respective supervising lecturers. • Basics of user studies (possible applications, definition of research hypothesis) • Study design (dependent/independent variables, laboratory vs. field studies, within-groups/between-groups design) • Planning of experiments (different methods, “fidelity” of an experiment, software/hardware prototypes, Wizard of Oz studies, qualitative surveys/quantitative measurements, ethical aspects, role of an institutional review board (IRB)) • Study implementation (preparation, briefing/debriefing, finding and inviting test subjects, determining group size, learning effects, measuring variables/data collection) • Qualitative data analysis (content analysis, evaluation with MAXQDA/NVIVO, preparation and presentation of results, revision/fine-tuning in Illustrator) • Quantitative data analysis (reflection on the research hypothesis, use of SPSS for statistical evaluations, parametric/nonparametric statistics, correct choice of method, presentation and interpretation of results) • Written/oral presentation of the results (preparation of results, peer review process, final conference-style presentation)

This course provides a practical introduction to the integration of generative AI and “vibe coding” into UX work-flows. Students will: • Explore foundational concepts of Generative AI, focusing on LLMs. • Learn techniques of prompt engineering to effectively direct AI tools. • Apply AI-driven methods to enhance UX research processes, from data collection to synthesis and prototyping. • Utilize AI-assisted development platforms, such as ChatGPT, Replit or Cursor, to prototype interactive user interfaces, tools and applications. • Investigate the emerging paradigm of Agentic AI and its potential to autonomously resolve user experience challenges. • Undertake a comprehensive final project, culminating in designing, building, and deploying a specialized AI agent addressing a defined UX issue.

Student Project Details

Coming soon

Interested?

Contact me if you’d like me to teach this course to you or your audience.

CV

Education

2012 International Ph.D in Computer Science, University of the Basque Country & Clemson University
- Supervisors: Karmele Lopez Ipina & Juan E. Gilbert
- Graduated Summa Cum Laude
2011 Advance Graduate Research in Engineering, University of Burgos
- Graduated Summa Cum Laude
2006 B.S. in Communication Sciences, University of Burgos
- Graduated Summa Cum Laude

Work experience

01/2025 - Present: Profesor of Human-centered Intelligent Systems
- THI, Computer Science Faculty, AI Motion Institute
12/2021 - Present: Principal Engineer, Technical Assistant to Intel Labs Director
- Intel Corporation, Intel Labs, Autonomous Driving Research
09/2014 - 12/2021: Sr. Research Scientist
- Intel Corporation, Intel Labs, Autonomous Driving Research
11/2013 - 09/2014 : Sr. Manager, IT Projects
- BMW China
12/2012 - 11/2013 : Manager, Connected Drive Asia Pacific
- BMW China
02/2012 - 12/2012 : IT Architect
- BMW AG
09/2009 - 02/2012 : Research Associate
- BMW USA, Greenville, SC
09/2009 - 02/2012 : Graudate Research Assistant
- Clemson University, School of Computing

Skills

Research
- Automotive
- Automated Driving
- Advance Driving Assistance Systems
- Safety
- Perception
- Planning
- Connected Vehicles - V2X
- Human Factors
- Human-Machine Interactions
- Driving Monitoring Systems
Technical
- Programming
- Data Analytics
- Product Development
- Prototyping
- Problem Solving
Languages
- Spanish - Native
- English / German - Fluent
- Chinese - Basic

Publications

Voice interfaced vehicle user help

Ignacio Alvarez et al.. (2010). Voice interfaced vehicle user help. Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

Are educational video games all they are cracked up to be

Ignacio Alvarez et al.. (2011). Are educational video games all they are cracked up to be published in International Journal of Learning Technology

Autonui a workshop on automotive natural user interfaces

Ignacio Alvarez et al.. (2011). Autonui a workshop on automotive natural user interfaces.in Autoui 2011

Designing driver centric natural voice user interfaces

Ignacio Alvarez et al.. (2011). Designing driver centric natural voice user interfaces. Adjunct Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications. online.

iHelp, the Ubiquitous Vehicle User Help

Ignacio Alvarez, Hans-Peter Fischer. (2011). iHelp, the Ubiquitous Vehicle User Help. Proceedings of Interact 2011.

AutoNUI: 2nd Workshop on Automotive Natural User Interfaces

Ignacio Alvarez et al.. (2012). AutoNUI: 2nd Workshop on Automotive Natural User Interfaces.Adjunct Proceedings of the 4th International Conference on Automotive User Interfaces

Contribution to the development of intelligent conversational assistants in automotive environments

Ignacio Javier Álvarez Martínez. (2012). Contribution to the development of intelligent conversational assistants in automotive environments. EHU.

Emotional Adaptive Vehicle User Interfaces, Moderating negative effects of failed technology interactions while driving

Ignacio Alvarez et al.. (2012). Emotional Adaptive Vehicle User Interfaces: moderating negative effects of failed technology interactions while driving. Adjunct Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI 2012).

Evaluating the ergonomics of BCI devices for research and experimentation

Joshua I Ekandem et al.. (2012). Evaluating the ergonomics of BCI devices for research and experimentation. Ergonomics.

The voice user help, a smart vehicle assistant for the elderly

Ignacio Alvarez, Miren Karmele López-de-Ipiña, Juan E Gilbert. (2012). The voice user help, a smart vehicle assistant for the elderly. International Conference on Ubiquitous Computing and Ambient Intelligence.

Response to Letter by Padulo and Ardigò

Joshua I Ekandem et al.. (2014). Response to Letter by Padulo and Ardigò. Ergonomics.

Social, natural, and peripheral interactions: Together and separate

Ignacio Alvarez et al.. (2014). Social, natural, and peripheral interactions: Together and separate. Proceedings of AutomotiveUI 2014.

The Social Car: Socially-inspired Mechanisms for Future Mobility Services

Ignacio Alvarez et al.. (2014). The Social Car: Socially-inspired Mechanisms for Future Mobility Services. Pervasive and Mobile Computing.

Help on the road: Effects of vehicle manual consultation in driving performance across modalities

Ignacio Alvarez et al.. (2015). Help on the road: Effects of vehicle manual consultation in driving performance across modalities. International journal of human-computer studies.

Prototyping adaptive automotive UX: A design pedagogy approach

Ignacio Alvarez et al.. (2015). Prototyping adaptive automotive UX: A design pedagogy approach. Extended Proceedings of the 7th International Conference on Automotive User.

Report on the in-vehicle auditory interactions workshop: Taxonomy, challenges, and approaches

Ignacio Alvarez et al.. (2015). Report on the in-vehicle auditory interactions workshop: Taxonomy, challenges, and approaches. Adjunct Proceedings AutomotiveUI ’15.

Skyline: a rapid prototyping driving simulator for user experience

Ignacio Alvarez et al.. (2015). Skyline: a rapid prototyping driving simulator for user experience. AutomotiveUI 2015.

Workshop on Adaptive Ambient In-Vehicle Displays and Interactions

Andreas Löcken et al.. (2015). Workshop on Adaptive Ambient In-Vehicle Displays and Interactions. Automotive UI 2015.

Workshop on Practical Experiences in Measuring and Modeling Drivers and Driver-Vehicle Interactions

Andreas Riener et al.. (2015). Workshop on Practical Experiences in Measuring and Modeling Drivers and Driver-Vehicle Interactions. Automotive UI 2015.

1st workshop on ethically inspired user interfaces for automated driving

Andreas Riener et al.. (2016). 1st workshop on ethically inspired user interfaces for automated driving. Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

1st workshop on situational awareness in semi-automated vehicles

Rod McCall et al.. (2016). 1st workshop on situational awareness in semi-automated vehicles. Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

Automotive user interfaces in the age of automation (Dagstuhl Seminar 16262)

Andreas Riener, Susanne Boll, Andrew L Kun. (2016). Automotive user interfaces in the age of automation (Dagstuhl Seminar 16262). Dagstuhl reports.

Autonomous hmi made easy: Prototyping reactive in-cabin aware hmis

Ignacio Alvarez et al.. (2016). Autonomous hmi made easy: Prototyping reactive in-cabin aware hmis. Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’16).

9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proceedings

Ignacio Alvarez et al.. (2017). 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proceedings. Automotive UI 2017.

Driver in the loop: Best practices in automotive sensing and feedback mechanisms

Ignacio Alvarez et al.. (2017). Driver in the loop: Best practices in automotive sensing and feedback mechanisms. Automotive user interfaces: creating interactive experiences in the car.

Skyline: A Platform Towards Scalable UX-Centric In-Vehicle HMI Development

Ignacio Alvarez et al.. (2017). Skyline: A Platform Towards Scalable UX-Centric In-Vehicle HMI Development. International Journal of Mobile Human Computer Interaction (IJMHCI).

Socializing under the influence of distracted driving: a study of the effects of in-vehicle and outside-of-the-vehicle communication while driving

Ignacio Alvarez et al.. (2017). Socializing under the influence of distracted driving: a study of the effects of in-vehicle and outside-of-the-vehicle communication while driving. Springer Nature Advances in Human Aspects of Transportation.

The insight–prototype–product cycle best practices and processes to iteratively advance in-vehicle interactive experiences development

Ignacio Alvarez et al.. (2017). The insight–prototype–product cycle best practices and processes to iteratively advance in-vehicle interactive experiences development. Automotive User Interfaces: Creating Interactive Experiences in the Car.

Towards Adaptive Ambient In-Vehicle Displays and Interactions: Insights and Design Guidelines from the 2015 AutomotiveUI Dedicated Workshop

Ignacio Alvarez et al.. (2017). Towards Adaptive Ambient In-Vehicle Displays and Interactions: Insights and Design Guidelines from the 2015 AutomotiveUI Dedicated Workshop. Automotive User Interfaces: Creating Interactive Experiences in the Car.

Workshop on user-centered design for automated driving systems

Anna-Katharina Frison et al.. (2017). Workshop on user-centered design for automated driving systems. Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

An international survey on automated and electric vehicles: Austria, Germany, South Korea, and USA

Ignacio Alvarez et al.. (2018). An international survey on automated and electric vehicles: Austria, Germany, South Korea, and USA. Springer Nature.

ARV 2018: 2nd Workshop on Augmented Reality for Intelligent Vehicles

Ignacio Alvarez et al.. (2018). ARV 2018: 2nd Workshop on Augmented Reality for Intelligent Vehicles. Adjunct Proceedings of the 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’18).

AutomotiveUI

Ignacio Alvarez et al.. (2018). AutomotiveUI. Automotive UI 2018.

Chairs

Ignacio Alvarez et al.. (2018). Chairs. Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

Emotional GaRage: A workshop on in-car emotion recognition and regulation

Ignacio Alvarez et al.. (2018). Emotional GaRage: A workshop on in-car emotion recognition and regulation. Adjunct Proceedings of the 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications AutomotiveUI 2018.

“Peace of Mind”, An Experiential Safety Framework for Automated Driving Technology Interactions

Marina Strano et al.. (2018). “Peace of Mind”, An Experiential Safety Framework for Automated Driving Technology Interactions. 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

Theo, take a right… uh… left: Conversational Route Negotiations with Autonomous Driving Assistants

Ignacio Alvarez et al.. (2018). Theo, take a right... uh... left: Conversational Route Negotiations with Autonomous Driving Assistants. Adjunct Proceedings of the 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’18).

Towards understanding emotional reactions of driver-passenger dyads in automated driving

Nese Alyuz et al.. (2018). Towards understanding emotional reactions of driver-passenger dyads in automated driving. 13th IEEE International Conference on Automatic Face & Gesture Recognition.

Workshop on designing highly automated driving systems as radical innovation

Anna-Katharina Frison et al.. (2018). Workshop on designing highly automated driving systems as radical innovation. Adjunct Proceedings of the 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’18).

2nd special session on solving the automated vehicle safety assurance challenge

Maria Soledad Elli et al.. (2019). 2nd special session on solving the automated vehicle safety assurance challenge. International Conference on Intelligent Transportation Systems (ITSC).

Design of a misbehavior detection system for objects based shared perception V2X applications

Ignacio Alvarez et al.. (2019). Design of a misbehavior detection system for objects based shared perception V2X applications. IEEE Intelligent Transportation Systems Conference.

Human-system cooperation in automated driving

Francesco Biondi, Ignacio Alvarez, Kyeong-Ah Jeong. (2019). Human-system cooperation in automated driving. No venue listed.

Human–vehicle cooperation in automated driving: A multidisciplinary review and appraisal

Ignacio Alvarez et al.. (2019). Human–vehicle cooperation in automated driving: A multidisciplinary review and appraisal. International Journal of Human Computer Interaction.

Object-level perception sharing among connected vehicles

Moreno Ambrosin et al.. (2019). Object-level perception sharing among connected vehicles. 2019 IEEE Intelligent Transportation Systems Conference (ITSC).

The SKYNIVI experience: evoking startle and frustration in dyads and single drivers

Ignacio Alvarez et al.. (2019). The SKYNIVI experience: evoking startle and frustration in dyads and single drivers. Proceedings of the IEEE International Conference in Intelligent Trasnportations Systems 2019.

Towards standardization of AV safety: C++ library for responsibility sensitive safety

Ignacio Alvarez et al.. (2019). Towards standardization of AV safety: C++ library for responsibility sensitive safety. Proceedings of the IEEE Intelligent Vehicle Symposium 2019.

Agents, environments, scenarios: A framework for examining models and simulations of human-vehicle interaction

Ignacio Alvarez et al.. (2020). Agents, environments, scenarios: A framework for examining models and simulations of human-vehicle interaction. Transportation research interdisciplinary perspectives.

Auto-UI Global Perspectives

Ignacio Alvarez et al.. (2020). Auto-UI Global Perspectives. ACM IX Interaction Journal.

Emotion GaRage Vol. II A Workshop on Affective In-Vehicle Display Design

Ignacio Alvarez et al.. (2020). Emotion GaRage Vol. II A Workshop on Affective In-Vehicle Display Design. Proceedings of 12th International Conference in Automotive User Interfaces and Interactive Vehicular Applications.

Evaluation of responsibility-sensitive safety (rss) model based on human-in-the-loop driving simulation

Chen Chai et al.. (2020). Evaluation of responsibility-sensitive safety (rss) model based on human-in-the-loop driving simulation. In proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems.

How safe is safe enough? Automatic safety constraints boundary estimation for decision-making in automated vehicles

Alëna Rodionova et al.. (2020). How safe is safe enough? Automatic safety constraints boundary estimation for decision-making in automated vehicles. Proceedings of IEEE Intelligent Vehicle Symposium 2020.

PURSS: Towards Perceptual Uncertainty Aware Responsibility Sensitive Safety with ML.

Ignacio Alvarez et al.. (2020). PURSS: Towards Perceptual Uncertainty Aware Responsibility Sensitive Safety with ML.. Proceedings of 2020 AAAI Conference.

MISO-V: Misbehavior detection for collective perception services in vehicular communications

Ignacio Alvarez et al.. (2021). MISO-V: Misbehavior detection for collective perception services in vehicular communications. Proceedings of IEEE Intelligent Transportation Systems Conference 2021.

On responsibility sensitive safety in car-following situations-a parameter analysis on german highways

Maximilian Naumann et al.. (2021). On responsibility sensitive safety in car-following situations-a parameter analysis on german highways. In 2021 Proceedings of IEEE Intelligent Vehicles Symposium (IV).

To err is human: The role of human derived safety metrics in an age of automated vehicles

Ignacio Alvarez et al.. (2021). To err is human: The role of human derived safety metrics in an age of automated vehicles. SAE Technical Paper.

Driver emotions in automated vehicles

Shabnam FakhrHosseini et al.. (2022). Driver emotions in automated vehicles. User Experience Design in the Era of Automated Driving.

Emotion GaRage Vol. III: A Workshop on Affective In-Vehicle Display Applications

Ignacio Alvarez et al.. (2022). Emotion GaRage Vol. III: A Workshop on Affective In-Vehicle Display Applications. Adjunct Proceedings of the 14th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

Empathic vehicle design: Use cases and design directions from two workshops

Ignacio Alvarez et al.. (2022). Empathic vehicle design: Use cases and design directions from two workshops. CHI 2022.

IEEE Standard for Assumptions in Safety-Related Models for Automated Driving Systems

ITS IEEE VT et al.. (2022). IEEE Standard for Assumptions in Safety-Related Models for Automated Driving Systems. No venue listed.

Quantifying Error Propagation in Multi-Stage Perception System of Autonomous Vehicles via Physics-Based Simulation

Fenglian Pan et al.. (2022). Quantifying Error Propagation in Multi-Stage Perception System of Autonomous Vehicles via Physics-Based Simulation. Proceedings of the IEEE 2022 Winter Simulation Conference.

Special Issue

Ignacio Alvarez et al.. (2022). Special Issue. MDPI Sensors.

User experience design in the era of automated driving

Andreas Riener, Myounghoon Jeon, Ignacio Alvarez. (2022). User experience design in the era of automated driving. Springer Studies in Computational 980.

White Paper-Literature Review on Kinematic Properties of Road Users for Use on Safety-Related Models for Automated Driving Systems

ITS IEEE VT et al.. (2022). White Paper-Literature Review on Kinematic Properties of Road Users for Use on Safety-Related Models for Automated Driving Systems. IEEE Standards.

Application of Responsibility-Sensitive Safety in Areas with Limited Visibility: Occlusions in RSS

Ignacio Alvarez et al.. (2023). Application of Responsibility-Sensitive Safety in Areas with Limited Visibility: Occlusions in RSS. 2023 IEEE 26th International Conference on Intelligent Transportation Systems.

Emotion GaRage Vol. IV: Creating empathic in-vehicle interfaces with generative AIs for automated vehicle contexts

Ignacio Alvarez et al.. (2023). Emotion GaRage Vol. IV: Creating empathic in-vehicle interfaces with generative AIs for automated vehicle contexts. Proceedings of Automotive UI 2023.

“Play Your Anger”: A report on the empathic in-vehicle interface workshop

Ignacio Alvarez et al.. (2023). “Play Your Anger”: A report on the empathic in-vehicle interface workshop. Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

RSS Demonstrator: a Tool for User Experience Interactions with Automated Driving Safety Models

Ignacio Alvarez et al.. (2023). RSS Demonstrator: a Tool for User Experience Interactions with Automated Driving Safety Models. Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

Inside Out: Emotion GaRage Vol. V

Ignacio Alvarez et al.. (2024). Inside Out: Emotion GaRage Vol. V. Adjunct Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

Reliability modeling for perception systems in autonomous vehicles: A recursive event-triggering point process approach

Ignacio Alvarez et al.. (2024). Reliability modeling for perception systems in autonomous vehicles: A recursive event-triggering point process approach. Transportation Research Part C Emerging Technologies.

Talks

Challenges and Opportunities in Automated Driving in the Era of Agentic AI

May 23, 2025

University Invited Lecture at Universidad Carlos 3 Madrid (UCM3), Master de Movilidad Cooperative Conectada, Madrid, Spain

Teaching

Study design and implementation of UX tests: AV harrassment

Study design and implementation of UX tests: Emphatic Car

Artificial Intelligence: LLM-based Agent for Driver Sleepiness Detection and Mitigation in Automotive Systems

Natural User Interfaces: Enhancing Autonomous Vehicle Safety Awareness through Gamification

Vibe Coding for UX Designers

Service and leadership

IEEE Sr. Member
ACM Member
IJHCI - Board Member
AutoUI - SC Member
IEEE ITSS - Member

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.mfp-close{color:#fff;right:-6px;text-align:right;padding-right:6px;width:100%}.mfp-counter{position:absolute;top:0;right:0;color:#ccc;font-size:12px;line-height:18px}.mfp-arrow{position:absolute;opacity:1;filter:alpha(opacity=100);margin:0;top:50%;margin-top:-55px;padding:0;width:90px;height:110px;-webkit-tap-highlight-color:transparent}.mfp-arrow:active{margin-top:-54px}.mfp-arrow:hover,.mfp-arrow:focus{opacity:1;filter:alpha(opacity=100)}.mfp-arrow:before,.mfp-arrow:after,.mfp-arrow .mfp-b,.mfp-arrow .mfp-a{content:'';display:block;width:0;height:0;position:absolute;left:0;top:0;margin-top:35px;margin-left:35px;border:medium inset transparent}.mfp-arrow:after,.mfp-arrow .mfp-a{border-top-width:13px;border-bottom-width:13px;top:8px}.mfp-arrow:before,.mfp-arrow .mfp-b{border-top-width:21px;border-bottom-width:21px;opacity:0.7}.mfp-arrow-left{left:0}.mfp-arrow-left:after,.mfp-arrow-left .mfp-a{border-right:17px solid #fff;margin-left:31px}.mfp-arrow-left:before,.mfp-arrow-left .mfp-b{margin-left:25px;border-right:27px solid #fff}.mfp-arrow-right{right:0}.mfp-arrow-right:after,.mfp-arrow-right .mfp-a{border-left:17px solid #fff;margin-left:39px}.mfp-arrow-right:before,.mfp-arrow-right .mfp-b{border-left:27px solid #fff}.mfp-iframe-holder{padding-top:40px;padding-bottom:40px}.mfp-iframe-holder .mfp-content{line-height:0;width:100%;max-width:900px}.mfp-iframe-holder .mfp-close{top:-40px}.mfp-iframe-scaler{width:100%;height:0;overflow:hidden;padding-top:56.25%}.mfp-iframe-scaler iframe{position:absolute;display:block;top:0;left:0;width:100%;height:100%;box-shadow:0 0 8px rgba(0,0,0,0.6);background:#000}img.mfp-img{width:auto;max-width:100%;height:auto;display:block;line-height:0;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;padding:40px 0 40px;margin:0 auto}.mfp-figure{line-height:0}.mfp-figure:after{content:'';position:absolute;left:0;top:40px;bottom:40px;display:block;right:0;width:auto;height:auto;z-index:-1;box-shadow:0 0 8px rgba(0,0,0,0.6);background:#444}.mfp-figure small{color:#bdbdbd;display:block;font-size:12px;line-height:14px}.mfp-figure figure{margin:0}.mfp-figure figcaption{margin-top:0;margin-bottom:0}.mfp-bottom-bar{margin-top:-36px;position:absolute;top:100%;left:0;width:100%;cursor:auto}.mfp-title{text-align:left;line-height:18px;color:#f3f3f3;word-wrap:break-word;padding-right:36px}.mfp-image-holder .mfp-content{max-width:100%}.mfp-gallery .mfp-image-holder .mfp-figure{cursor:pointer}@media screen and (max-width: 800px) and (orientation: landscape), screen and (max-height: 300px){.mfp-img-mobile .mfp-image-holder{padding-left:0;padding-right:0}.mfp-img-mobile img.mfp-img{padding:0}.mfp-img-mobile .mfp-figure:after{top:0;bottom:0}.mfp-img-mobile .mfp-figure small{display:inline;margin-left:5px}.mfp-img-mobile .mfp-bottom-bar{background:rgba(0,0,0,0.6);bottom:0;margin:0;top:auto;padding:3px 5px;position:fixed;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.mfp-img-mobile .mfp-bottom-bar:empty{padding:0}.mfp-img-mobile .mfp-counter{right:5px;top:3px}.mfp-img-mobile .mfp-close{top:0;right:0;width:35px;height:35px;line-height:35px;background:rgba(0,0,0,0.6);position:fixed;text-align:center;padding:0}}@media all and (max-width: 900px){.mfp-arrow{-webkit-transform:scale(0.75);transform:scale(0.75)}.mfp-arrow-left{-webkit-transform-origin:0;transform-origin:0}.mfp-arrow-right{-webkit-transform-origin:100%;transform-origin:100%}.mfp-container{padding-left:6px;padding-right:6px}}.mfp-ie7 .mfp-img{padding:0}.mfp-ie7 .mfp-bottom-bar{width:600px;left:50%;margin-left:-300px;margin-top:5px;padding-bottom:5px}.mfp-ie7 .mfp-container{padding:0}.mfp-ie7 .mfp-content{padding-top:44px}.mfp-ie7 .mfp-close{top:0;right:0;padding-top:0}@media print{.masthead,.toc,.page__share,.page__related,.ads,.page__footer{display:none}}:root{--background-color: #ffffff;--text-color: #000000;--link-color: #0366d6;--masthead-background: #ffffff;--footer-background: #f0f0f0;--footer-border: #e0e0e0}[data-theme="dark"]{--background-color: #121212;--text-color: #e4e4e4;--link-color: #58a6ff;--masthead-background: transparent;--footer-background: #1e1e1e;--footer-border: #333333}body{background-color:var(--background-color);color:var(--text-color)}a{color:var(--link-color)}.masthead{background-color:var(--masthead-background);color:var(--text-color)}.page__footer{background-color:var(--footer-background);border-top:1px solid var(--footer-border)}.theme-toggle{background:none;border:1px solid rgba(52,152,219,0.3);color:#3498db;padding:0.5rem;border-radius:8px;cursor:pointer;transition:all 0.3s ease;position:relative;overflow:hidden}.theme-toggle:hover{background:rgba(52,152,219,0.1);border-color:#3498db}.theme-toggle .moon-icon,.theme-toggle .sun-icon{font-size:1.2rem}.theme-toggle .sun-icon{display:none}[data-theme="dark"] .theme-toggle .moon-icon{display:none}[data-theme="dark"] .theme-toggle .sun-icon{display:inline}.author__urls .fa-map-marker{color:#e74c3c !important}[data-theme="dark"] .author__urls .fa-github,[data-theme="dark"] .author__urls .fab.fa-github{color:#ffffff !important}.author__urls .fa-github,.author__urls .fab.fa-github{color:#171516 !important}.author__urls .fa-map-marker{color:#e74c3c !important}.social-icons .fa-github,.social-icons .fab.fa-github{color:#171516 !important}[data-theme="dark"] .social-icons .fa-github,[data-theme="dark"] .social-icons .fab.fa-github{color:#ffffff !important}

Markdown

Locations of key files/directories

Basic config options: _config.yml
Top navigation bar config: _data/navigation.yml
Single pages: _pages/
Collections of pages are .md or .html files in:
- _publications/
- _portfolio/
- _posts/
- _teaching/
- _talks/
Footer: _includes/footer.html
Static files (like PDFs): /files/
Profile image (can set in _config.yml): images/profile.png

Tips and hints

Name a file “.md” to have it render in markdown, name it “.html” to render in HTML.
Go to the commit list (on your repo) to find the last version Github built with Jekyll.
- Green check: successful build
- Orange circle: building
- Red X: error
- No icon: not built

Resources

Liquid syntax guide

Markdown guide

Header three

Header four

Header five

Header six

Blockquotes

Single line blockquote:

Quotes are cool.

Tables

Table 1

Entry	Item
John Doe	2016	Description of the item in the list
Jane Doe	2019	Description of the item in the list
Doe Doe	2022	Description of the item in the list

Table 2

Header1	Header2	Header3
cell1	cell2	cell3
cell4	cell5	cell6
cell1	cell2	cell3
cell4	cell5	cell6
Foot1	Foot2	Foot3

Definition Lists

Definition List Title: Definition list division.
Startup: A startup company or startup is a company or temporary organization designed to search for a repeatable and scalable business model.
#dowork: Coined by Rob Dyrdek and his personal body guard Christopher “Big Black” Boykins, “Do Work” works as a self motivator, to motivating your friends.
Do It Live: I’ll let Bill O’Reilly explain this one.

Unordered Lists (Nested)

List item one
- List item one
  - List item one
  - List item two
  - List item three
  - List item four
- List item two
- List item three
- List item four
List item two
List item three
List item four

Ordered List (Nested)

List item one
1. List item one
  1. List item one
  2. List item two
  3. List item three
  4. List item four
2. List item two
3. List item three
4. List item four
List item two
List item three
List item four

Buttons

Make any link standout more when applying the .btn class.

Notices

Watch out! You can also add notices by appending {: .notice} to a paragraph.

HTML Tags

Address Tag

1 Infinite Loop
Cupertino, CA 95014
United States

Anchor Tag (aka. Link)

This is an example of a link.

Abbreviation Tag

The abbreviation CSS stands for “Cascading Style Sheets”.

Cite Tag

“Code is poetry.” —Automattic

Code Tag

You will learn later on in these tests that word-wrap: break-word; will be your best friend.

Strike Tag

This tag will let you ~~strikeout text~~.

Emphasize Tag

The emphasize tag should italicize text.

Insert Tag

This tag should denote inserted text.

Keyboard Tag

This scarcely known tag emulates keyboard text, which is usually styled like the <code> tag.

Preformatted Tag

This tag styles large blocks of code.

.post-title {
  margin: 0 0 5px;
  font-weight: bold;
  font-size: 38px;
  line-height: 1.2;
  and here's a line of some really, really, really, really long text, just to see how the PRE tag handles it and to find out how it overflows;
}

Quote Tag

Developers, developers, developers… –Steve Ballmer

Strong Tag

This tag shows bold text.

Subscript Tag

Getting our science styling on with H₂O, which should push the “2” down.

Superscript Tag

Still sticking with science and Isaac Newton’s E = MC², which should lift the 2 up.

Variable Tag

This allows you to denote variables.

Page not in menu

This is a page not in the menu. You can use markdown in this page.

Heading 1

Heading 2

Page Archive

Page Not Found

Sorry, but the page you were trying to view does not exist — perhaps you can try searching for it below.

About me

Shaping the Future of Mobility with Human-Centered AI

For over 15 years, I have turned idea concepts into production-ready intelligent systems at Intel and BMW. As a research professor, I am now pioneering the next wave of human-centric AI to build a safer, more intelligent mobility future. This site is an exploration of that journey at the intersection of AI, automotive, and user experience.

Prof. Dr. Alvarez

Archive Layout with Content

A variety of common markup showing how the theme styles them.

Header one

Header two

Header three

Header four

Header five

Header six

Blockquotes

Single line blockquote:

Quotes are cool.

Tables

Entry	Item
John Doe	2016	Description of the item in the list
Jane Doe	2019	Description of the item in the list
Doe Doe	2022	Description of the item in the list

Header1	Header2	Header3
cell1	cell2	cell3
cell4	cell5	cell6
cell1	cell2	cell3
cell4	cell5	cell6
Foot1	Foot2	Foot3

Definition Lists

Definition List Title: Definition list division.
Startup: A startup company or startup is a company or temporary organization designed to search for a repeatable and scalable business model.
#dowork: Coined by Rob Dyrdek and his personal body guard Christopher “Big Black” Boykins, “Do Work” works as a self motivator, to motivating your friends.
Do It Live: I’ll let Bill O’Reilly explain this one.

Unordered Lists (Nested)

List item one
- List item one
  - List item one
  - List item two
  - List item three
  - List item four
- List item two
- List item three
- List item four
List item two
List item three
List item four

Ordered List (Nested)

List item one
1. List item one
  1. List item one
  2. List item two
  3. List item three
  4. List item four
2. List item two
3. List item three
4. List item four
List item two
List item three
List item four

Buttons

Make any link standout more when applying the .btn class.

Notices

Watch out! You can also add notices by appending {: .notice} to a paragraph.

HTML Tags

Address Tag

1 Infinite Loop
Cupertino, CA 95014
United States

Anchor Tag (aka. Link)

This is an example of a link.

Abbreviation Tag

The abbreviation CSS stands for “Cascading Style Sheets”.

Cite Tag

“Code is poetry.” —Automattic

Code Tag

You will learn later on in these tests that word-wrap: break-word; will be your best friend.

Strike Tag

This tag will let you ~~strikeout text~~.

Emphasize Tag

The emphasize tag should italicize text.

Insert Tag

This tag should denote inserted text.

Keyboard Tag

This scarcely known tag emulates keyboard text, which is usually styled like the <code> tag.

Preformatted Tag

This tag styles large blocks of code.

.post-title {
  margin: 0 0 5px;
  font-weight: bold;
  font-size: 38px;
  line-height: 1.2;
  and here's a line of some really, really, really, really long text, just to see how the PRE tag handles it and to find out how it overflows;
}

Quote Tag

Developers, developers, developers… –Steve Ballmer

Strong Tag

This tag shows bold text.

Subscript Tag

Getting our science styling on with H₂O, which should push the “2” down.

Superscript Tag

Still sticking with science and Isaac Newton’s E = MC², which should lift the 2 up.

Variable Tag

This allows you to denote variables.

Page Not Found

Sorry, but the page you were trying to view does not exist — perhaps you can try searching for it below.

About me

Shaping the Future of Mobility with Human-Centered AI

For over 15 years, I have turned idea concepts into production-ready intelligent systems at Intel and BMW. As a research professor, I am now pioneering the next wave of human-centric AI to build a safer, more intelligent mobility future. This site is an exploration of that journey at the intersection of AI, automotive, and user experience.

Prof. Dr. Alvarez

Archive Layout with Content

A variety of common markup showing how the theme styles them. # Header one ## Header two ### Header three #### Header four ##### Header five ###### Header six ## Blockquotes Single line blockquote: > Quotes are cool. ## Tables | Entry | Item | | | -------- | ------ | ------------------------------------------------------------ | | [John Doe](#) | 2016 | Description of the item in the list | | [Jane Doe](#) | 2019 | Description of the item in the list | | [Doe Doe](#) | 2022 | Description of the item in the list | | Header1 | Header2 | Header3 | |:--------|:-------:|--------:| | cell1 | cell2 | cell3 | | cell4 | cell5 | cell6 | |-----------------------------| | cell1 | cell2 | cell3 | | cell4 | cell5 | cell6 | |=============================| | Foot1 | Foot2 | Foot3 | ## Definition Lists Definition List Title : Definition list division. Startup : A startup company or startup is a company or temporary organization designed to search for a repeatable and scalable business model. #dowork : Coined by Rob Dyrdek and his personal body guard Christopher "Big Black" Boykins, "Do Work" works as a self motivator, to motivating your friends. Do It Live : I'll let Bill O'Reilly [explain](https://www.youtube.com/watch?v=O_HyZ5aW76c "We'll Do It Live") this one. ## Unordered Lists (Nested) * List item one * List item one * List item one * List item two * List item three * List item four * List item two * List item three * List item four * List item two * List item three * List item four ## Ordered List (Nested) 1. List item one 1. List item one 1. List item one 2. List item two 3. List item three 4. List item four 2. List item two 3. List item three 4. List item four 2. List item two 3. List item three 4. List item four ## Buttons Make any link standout more when applying the `.btn` class. ## Notices **Watch out!** You can also add notices by appending `{: .notice}` to a paragraph. {: .notice} ## HTML Tags ### Address Tag

1 Infinite Loop
Cupertino, CA 95014
United States

### Anchor Tag (aka. Link) This is an example of a [link](http://github.com "Github"). ### Abbreviation Tag The abbreviation CSS stands for "Cascading Style Sheets". *[CSS]: Cascading Style Sheets ### Cite Tag "Code is poetry." ---Automattic ### Code Tag You will learn later on in these tests that `word-wrap: break-word;` will be your best friend. ### Strike Tag This tag will let you ~~strikeout text~~. ### Emphasize Tag The emphasize tag should _italicize_ text. ### Insert Tag This tag should denote inserted text. ### Keyboard Tag This scarcely known tag emulates keyboard text, which is usually styled like the `

` tag. ### Preformatted Tag This tag styles large blocks of code..post-title {
  margin: 0 0 5px;
  font-weight: bold;
  font-size: 38px;
  line-height: 1.2;
  and here's a line of some really, really, really, really long text, just to see how the PRE tag handles it and to find out how it overflows;
}
### Quote Tag Developers, developers, developers… –Steve Ballmer ### Strong Tag This tag shows **bold text**. ### Subscript Tag Getting our science styling on with H₂O, which should push the "2" down. ### Superscript Tag Still sticking with science and Isaac Newton's E = MC², which should lift the 2 up. ### Variable Tag This allows you to denote variables. {% include base_path %} {% for post in site.pages %} {% include archive-single.html %} {% endfor %} </article> </div> Posts by Category
{% include base_path %} {% include group-by-array collection=site.posts field="categories" %} {% for category in group_names %} {% assign posts = group_items[forloop.index0] %}{{ category }}
{% for post in posts %} {% include archive-single.html %} {% endfor %} {% endfor %}
 Posts by Collection
{% include base_path %} {% capture written_label %}'None'{% endcapture %} {% for collection in site.collections %} {% unless collection.output == false or collection.label == "posts" %} {% capture label %}{{ collection.label }}{% endcapture %} {% if label != written_label %}{{ label }}
{% capture written_label %}{{ label }}{% endcapture %} {% endif %} {% endunless %} {% for post in collection.docs %} {% unless collection.output == false or collection.label == "posts" %} {% include archive-single.html %} {% endunless %} {% endfor %} {% endfor %}
 CV
{% include base_path %} Education ====== * 2012 International Ph.D in Computer Science, University of the Basque Country & Clemson University * Supervisors: Karmele Lopez Ipina & Juan E. Gilbert * Graduated Summa Cum Laude * 2011 Advance Graduate Research in Engineering, University of Burgos * Graduated Summa Cum Laude * 2006 B.S. in Communication Sciences, University of Burgos * Graduated Summa Cum Laude Work experience ====== * 01/2025 - Present: Profesor of Human-centered Intelligent Systems * THI, Computer Science Faculty, AI Motion Institute * 12/2021 - Present: Principal Engineer, Technical Assistant to Intel Labs Director * Intel Corporation, Intel Labs, Autonomous Driving Research * 09/2014 - 12/2021: Sr. Research Scientist * Intel Corporation, Intel Labs, Autonomous Driving Research * 11/2013 - 09/2014 : Sr. Manager, IT Projects * BMW China * 12/2012 - 11/2013 : Manager, Connected Drive Asia Pacific * BMW China * 02/2012 - 12/2012 : IT Architect * BMW AG * 09/2009 - 02/2012 : Research Associate * BMW USA, Greenville, SC * 09/2009 - 02/2012 : Graudate Research Assistant * Clemson University, School of Computing Skills ====== * Research * Automotive * Automated Driving * Advance Driving Assistance Systems * Safety * Perception * Planning * Connected Vehicles - V2X * Human Factors * Human-Machine Interactions * Driving Monitoring Systems * Technical * Programming * Data Analytics * Product Development * Prototyping * Problem Solving * Languages * Spanish - Native * English / German - Fluent * Chinese - Basic Publications ======{% for post in site.publications %} {% include archive-single-cv.html %} {% endfor %}
Talks ======{% for post in site.talks %} {% include archive-single-talk-cv.html %} {% endfor %}
Teaching ======{% for post in site.teaching %} {% include archive-single-cv.html %} {% endfor %}
Service and leadership ====== * IEEE Sr. Member * ACM Member * IJHCI - Board Member * AutoUI - SC Member * IEEE ITSS - Member
 
/* * Minimal Mistakes Jekyll Theme * * - Michael Rose * - mademistakes.com * - https://twitter.com/mmistakes * */ @import "vendor/breakpoint/breakpoint"; // media query mixins @import "variables"; @import "mixins"; @import "vendor/susy/susy"; @import "reset"; @import "base"; @import "utilities"; @import "animations"; @import "tables"; @import "buttons"; @import "citations"; @import "notices"; @import "masthead"; @import "navigation"; @import "footer"; @import "syntax"; @import "forms"; @import "page"; @import "archive"; @import "sidebar"; @import "vendor/font-awesome/fontawesome"; @import "vendor/font-awesome/solid"; @import "vendor/font-awesome/brands"; @import "vendor/magnific-popup/magnific-popup"; @import "print"; @import "dark-theme";
 Markdown
## Locations of key files/directories * Basic config options: _config.yml * Top navigation bar config: _data/navigation.yml * Single pages: _pages/ * Collections of pages are .md or .html files in: * _publications/ * _portfolio/ * _posts/ * _teaching/ * _talks/ * Footer: _includes/footer.html * Static files (like PDFs): /files/ * Profile image (can set in _config.yml): images/profile.png ## Tips and hints * Name a file ".md" to have it render in markdown, name it ".html" to render in HTML. * Go to the [commit list](https://github.com/academicpages/academicpages.github.io/commits/master) (on your repo) to find the last version Github built with Jekyll. * Green check: successful build * Orange circle: building * Red X: error * No icon: not built ## Resources * [Liquid syntax guide](https://shopify.github.io/liquid/tags/control-flow/) ## Markdown guide ### Header three #### Header four ##### Header five ###### Header six ## Blockquotes Single line blockquote: > Quotes are cool. ## Tables ### Table 1 | Entry | Item | | | -------- | ------ | ------------------------------------------------------------ | | [John Doe](#) | 2016 | Description of the item in the list | | [Jane Doe](#) | 2019 | Description of the item in the list | | [Doe Doe](#) | 2022 | Description of the item in the list | ### Table 2 | Header1 | Header2 | Header3 | |:--------|:-------:|--------:| | cell1 | cell2 | cell3 | | cell4 | cell5 | cell6 | |-----------------------------| | cell1 | cell2 | cell3 | | cell4 | cell5 | cell6 | |=============================| | Foot1 | Foot2 | Foot3 | ## Definition Lists Definition List Title : Definition list division. Startup : A startup company or startup is a company or temporary organization designed to search for a repeatable and scalable business model. #dowork : Coined by Rob Dyrdek and his personal body guard Christopher "Big Black" Boykins, "Do Work" works as a self motivator, to motivating your friends. Do It Live : I'll let Bill O'Reilly [explain](https://www.youtube.com/watch?v=O_HyZ5aW76c "We'll Do It Live") this one. ## Unordered Lists (Nested) * List item one * List item one * List item one * List item two * List item three * List item four * List item two * List item three * List item four * List item two * List item three * List item four ## Ordered List (Nested) 1. List item one 1. List item one 1. List item one 2. List item two 3. List item three 4. List item four 2. List item two 3. List item three 4. List item four 2. List item two 3. List item three 4. List item four ## Buttons Make any link standout more when applying the `.btn` class. ## Notices **Watch out!** You can also add notices by appending `{: .notice}` to a paragraph. {: .notice} ## HTML Tags ### Address Tag 1 Infinite Loop
 Cupertino, CA 95014
 United States
### Anchor Tag (aka. Link) This is an example of a [link](http://github.com "Github"). ### Abbreviation Tag The abbreviation CSS stands for "Cascading Style Sheets". *[CSS]: Cascading Style Sheets ### Cite Tag "Code is poetry." ---Automattic ### Code Tag You will learn later on in these tests that `word-wrap: break-word;` will be your best friend. ### Strike Tag This tag will let you strikeout text. ### Emphasize Tag The emphasize tag should _italicize_ text. ### Insert Tag This tag should denote inserted text. ### Keyboard Tag This scarcely known tag emulates keyboard text, which is usually styled like the `` tag. ### Preformatted Tag This tag styles large blocks of code..post-title {
  margin: 0 0 5px;
  font-weight: bold;
  font-size: 38px;
  line-height: 1.2;
  and here's a line of some really, really, really, really long text, just to see how the PRE tag handles it and to find out how it overflows;
}
### Quote Tag Developers, developers, developers… –Steve Ballmer ### Strong Tag This tag shows **bold text**. ### Subscript Tag Getting our science styling on with H₂O, which should push the "2" down. ### Superscript Tag Still sticking with science and Isaac Newton's E = MC², which should lift the 2 up. ### Variable Tag This allows you to denote variables. </article> </div> Page not in menu
This is a page not in the menu. You can use markdown in this page. Heading 1 ====== Heading 2 ======
 Page Archive
{% include base_path %} {% for post in site.pages %} {% include archive-single.html %} {% endfor %}
 Patents
 {% include base_path %} {% assign postsByYear = site.patents | group_by_exp:"post", "post.date | date: '%Y'" | reverse %} {% for year_group in postsByYear %} <details class="year-details" {% if forloop.first %}open{% endif %}> {{ year_group.name }}
 {%- comment -%} Using 'publications-list-indent' for consistency, can be renamed {%- endcomment -%} {% for post in year_group.items reversed %} {%- comment -%} Using 'publication-item' for consistency, can be renamed {%- endcomment -%} {% include archive-single.html %}
{% endfor %}
</details> {% endfor %}
 Portfolio
{% include base_path %} {% for post in site.portfolio %} {% include archive-single.html %} {% endfor %}
 Publications
{% if author.googlescholar %} You can also find my articles on my Google Scholar profile. {% endif %}  {% include base_path %} {% assign postsByYear = site.publications | group_by_exp:"post", "post.date | date: '%Y'" | reverse %} {% for year_group in postsByYear %} <details class="year-details" {% if forloop.first %}open{% endif %}> {{ year_group.name }}
 {% for post in year_group.items reversed %} {% include archive-single.html %}
{% endfor %}
</details> {% endfor %}
 Executive Portfolio
 IGNACIO ALVAREZ
Technology Executive | Automotive & AI Leader
📍 Based in Germany · Open to Relocation Globally
   ignacio.alvarez@thi.de    LinkedIn 
   Download PDF Resume    Listen Resume Podcast 
 
Executive Summary
 Technology Executive with a track record of CTO-level impact in the Automotive & AI sectors. Proven leader with 15+ years of experience architecting and scaling intelligent systems from R&D to series production, driving 30% YoY R&D ROI amidst budget reductions, leading global R&D organizations of 100+ engineers, and launching platforms adopted by 1M+ users. Expert in defining technical vision that aligns with C-suite objectives, secures market leadership, and generates massive IP value (150+ patents).
Core Competencies
 Global R&D Leadership (100+ engineers) Technology Strategy & Roadmaps Productization & Tech Transfer Automated Driving Architecture Compute Platform Strategy Artificial Intelligence & Machine Learning Deep Learning & Generative AI Functional Safety Connected Vehicles (V2X) User-Centered Design Human–Machine Interface (HMI) Cyber-Physical Security Digital Twins & Large Scale Simulation Edge AI & Perception Stacks Behavior/Planning Verification & Validation Connected Vehicle Services Standards Leadership & Ecosystem Patent Strategy & Portfolio Development Cross-Functional Leadership Fluent in English, Spanish & German Basic Mandarin
Professional Experience
 Tenured Research Professor of Human-centered Intelligent Systems
Technische Hochschule Ingolstadt
2025–Present
 
  Tenured research professor at AI Motion Bavaria Research Institute. Leading European Research Initiatives in AI and Automotive. Sponsored by the High-Tech Agenda Program of the Government of Bavaria with a €3M grant to develop a research program in human-centered intelligent vehicles.
Challenge:
Establish and lead cutting-edge research initiatives in human-centered intelligent vehicles while securing substantial funding and building academic-industry partnerships.
Action:
Secured €3M grant from the High-Tech Agenda Program of the Government of Bavaria to develop comprehensive research programs in AI and Automotive. Established THI Doctoral School membership and developed advanced curriculum for Automotive AI Masters and User Experience Design programs.
Result:
Successfully positioned THI as a leading European research institution in AI and Automotive, with substantial funding secured and advanced educational programs established across Computer Science and Engineering faculties.
Principal Engineer & Executive Technical Advisor
Intel Corporation – Intel Labs
2021–2025
 
  Reporting to the Intel Labs Director. Tasked with maximizing R&D value amidst an average 8% yearly budget reduction. Advised C-suite on strategic technology investments, accelerating product impact and optimizing IP utilization. Drove an average 30% YoY increase in R&D ROI, reversing the impact of budget cuts.
Challenge:
Maximize R&D value and strategic impact while managing 8% yearly budget reductions across Intel Labs' global engineering organization, requiring innovative approaches to resource optimization and strategic technology investments.
Action:
Established unified research strategies and cross-functional collaboration across Intel Labs' 500+ person global engineering organization. Steered autonomous driving R&D strategy aligning 100+ multi-regional engineering resources. Served as key technology advisor for M&A, divestiture, and IPO activities within the automotive sector.
Result:
Achieved average 30% YoY increase in R&D ROI, reversing the impact of budget cuts across semiconductor design, packaging, systems, networking, software, algorithmic development and AI. Successfully architected and deployed production-grade automated driving safety systems, reducing validation cycles by ~20% and accelerating compliance with emerging safety regulations.
Senior Research Scientist & Autonomous Driving Lead
Intel Corporation – Intel Labs
2014–2021
 
  Directed development of a multimodal in-vehicle R&D platform ("Skyline") integrating driver monitoring, voice assistants, and AR displays. Supporting design winds of Intel Automotive technology by 10+ global automotive OEMs and showcased the platform at international industry forums.
Challenge:
Develop and commercialize a comprehensive multimodal in-vehicle R&D platform that integrates cutting-edge technologies while establishing Intel as a leading automotive technology partner and securing substantial research funding.
Action:
Led cross-institutional research collaborations, securing $5M+ in funding and partnerships. Transferred multiple key technologies to Intel's and Mobileye product teams. Led open-source contributions to industry bodies and international standards, bridging research to real-world impact.
Result:
Successfully deployed "Skyline" platform across 10+ global automotive OEMs, showcased at international forums (Google IO, Intel 360). Secured 50+ granted patents and 100+ pending in autonomous systems, AI and robotics. Published 70+ papers and built thought leadership in automotive AI safety and HMI.
Senior IT Manager & Connected Drive Lead
BMW Group
2012–2014
 
  Led a 100+ IT engineering division across Asia-Pacific, delivering next-generation connected vehicle services to drive rapid expansion into the region's fastest-growing market. Oversaw data centers, telematics, and in-car digital services for BMW and MINI brands in China and Asia Pacific markets.
Challenge:
Lead rapid expansion into Asia-Pacific's fastest-growing automotive market while ensuring 24/7 reliability and scalability of connected vehicle services during record growth, and differentiate BMW in a competitive luxury market through innovative in-car technology.
Action:
Managed 100+ IT engineering division across Asia-Pacific, overseeing data centers, telematics, and in-car digital services. Pioneered in-car voice and AI assistants, collaborating with partners like Nuance and Microsoft. Developed integrated multimodal user manual for BMW 7 series.
Result:
Achieved over 1 million new users in the first year, significantly enhancing customer engagement. Launched industry's first natural-language voice assistant and multimodal user manual, setting new standards for in-vehicle HMI and earning industry-wide commendations for innovation.
Advanced Research Roles
BMW IT Research Center and Clemson University, I-CAR
2009–2012
 
  Held advanced research roles with BMW Technology Office USA and Clemson University, developing functional prototypes for commercial systems. Contributed to the first proofs-of-concept for location-based services, voice-activated navigation, augmented reality user manuals, and driver assistance prototypes.
Challenge:
Develop cutting-edge automotive technology prototypes that can inform future product roadmaps while bridging academic research with commercial applications in the automotive industry.
Action:
Collaborated between BMW Technology Office USA and Clemson University to develop functional prototypes for location-based services, voice-activated navigation, augmented reality user manuals, and driver assistance systems.
Result:
Successfully created first proofs-of-concept that informed future BMW product roadmaps, establishing foundational technologies for connected vehicle services and advanced driver assistance systems.
Professional Leadership & Affiliations
  IEEE Intelligent Transportation Systems Society – Member of Board of Governors (elected role driving global ITS strategy)
  ACM AutomotiveUI Conference – Steering Committee Member (shaping annual agenda for automotive UX innovation)
  Editorial Boards – Board Member for IEEE ITS Magazine and Int. Journal of Human-Computer Interaction
  Senior Member, IEEE and Member, ACM, SAE (recognized professional standing and contributions in tech)
Thought Leadership & Innovation
 Patents
Holder of 150+ patents (50+ granted) in autonomous systems, AI, and robotics, shaping the core IP strategy for market-leading mobility solutions.
View Patent Portfolio 
 Publications
Author of 70+ publications in top-tier IEEE and ACM venues and editor of multiple books on autonomous system engineering.
View Publications 
 Industry Standards
Key contributor to the definition of global safety and communication standards for autonomous vehicles through leadership roles in IEEE and ETSI.
View Standards Work 
Education
Ph.D. in Computer Science (Automotive Intelligent Systems)
University of the Basque Country, Spain & Clemson University, USA, 2012
Summa Cum Laude
Joint international doctoral program focusing on conversational AI for vehicles.
M.Sc. in Media and Information Technology
Hochschule Offenburg, Germany, 2009
Coursework in multimedia systems and human-computer interaction.
B.Sc. in Communication Sciences
University of Burgos, Spain, 2006
Summa Cum Laude
Minor in Telecommunications.
 Sitemap
{% include base_path %} A list of all the posts and pages found on the site. For you robots out there is an [XML version]({{ base_path }}/sitemap.xml) available for digesting as well.Pages
{% for post in site.pages %} {% include archive-single.html %} {% endfor %}Posts
{% for post in site.posts %} {% include archive-single.html %} {% endfor %} {% capture written_label %}'None'{% endcapture %} {% for collection in site.collections %} {% unless collection.output == false or collection.label == "posts" %} {% capture label %}{{ collection.label }}{% endcapture %} {% if label != written_label %}{{ label }}
{% capture written_label %}{{ label }}{% endcapture %} {% endif %} {% endunless %} {% for post in collection.docs %} {% unless collection.output == false or collection.label == "posts" %} {% include archive-single.html %} {% endunless %} {% endfor %} {% endfor %}
 Posts by Tags
{% include base_path %} {% include group-by-array collection=site.posts field="tags" %} {% for tag in group_names %} {% assign posts = group_items[forloop.index0] %}{{ tag }}
{% for post in posts %} {% include archive-single.html %} {% endfor %} {% endfor %}
 Talk map
This map is generated from a Jupyter Notebook file in /_talks/talkmap.ipynb, which mines the location fields in the .md files in _talks/.
 Talks and presentations
{% if site.talkmap_link == true %}See a map of all the places I've given a talk!
{% endif %} {% for post in site.talks reversed %} {% include archive-single-talk.html type='talk' %} {% endfor %}
 Talks
 {% include base_path %} {% assign postsByYear = site.talks | group_by_exp:"post", "post.date | date: '%Y'" | reverse %} {% for year_group in postsByYear %} <details class="year-details" {% if forloop.first %}open{% endif %}> {{ year_group.name }}
 {% for post in year_group.items reversed %} {% include archive-single-talk.html type='talk' %}
{% endfor %}
</details> {% endfor %}
 Teaching
{% include base_path %} {% for post in site.teaching reversed %} {% include archive-single.html %} {% endfor %}
 Teaching
 {% include base_path %} {% assign postsByYear = site.teaching | group_by_exp:"post", "post.date | date: '%Y'" | reverse %} {% for year_group in postsByYear %} <details class="year-details" {% if forloop.first %}open{% endif %}> {{ year_group.name }}
 {% for post in year_group.items reversed %} {% include archive-single-teaching.html type='teaching' %}
{% endfor %}
</details> {% endfor %}
 Terms and Privacy Policy
{% include base_path %} {% include toc %} ## Privacy Policy The privacy of my visitors is extremely important. This Privacy Policy outlines the types of personal information that is received and collected and how it is used. First and foremost, I will never share your email address or any other personal information to anyone without your direct consent. ### Log Files Like many other websites, this site uses log files to help learn about when, from where, and how often traffic flows to this site. The information in these log files include: * Internet Protocol addresses (IP) * Types of browser * Internet Service Provider (ISP) * Date and time stamp * Referring and exit pages * Number of clicks All of this information is not linked to anything that is personally identifiable. ### Cookies and Web Beacons When you visit this site "convenience" cookies are stored on your computer when you submit a comment to help you log in faster to [Disqus](http://disqus.com) the next time you leave a comment. Third-party advertisers may also place and read cookies on your browser and/or use web beacons to collect information. This site has no access or control over these cookies. You should review the respective privacy policies on any and all third-party ad servers for more information regarding their practices and how to opt-out. If you wish to disable cookies, you may do so through your web browser options. Instructions for doing so can be found on the specific web browsers' websites. #### Google Analytics Google Analytics is a web analytics tool I use to help understand how visitors engage with this website. It reports website trends using cookies and web beacons without identifying individual visitors. You can read [Google Analytics Privacy Policy](http://www.google.com/analytics/learn/privacy.html).
 Blog posts
{% include base_path %} {% capture written_year %}'None'{% endcapture %} {% for post in site.posts %} {% capture year %}{{ post.date | date: '%Y' }}{% endcapture %} {% if year != written_year %}{{ year }}
{% capture written_year %}{{ year }}{% endcapture %} {% endif %} {% include archive-single.html %} {% endfor %}
 
@import "jekyll-theme-primer";
 
 
 
 
 
 
 
 
 
{"/about/":"https://ignacioalvmar.com/","/about.html":"https://ignacioalvmar.com/","/md/":"https://ignacioalvmar.com/markdown/","/markdown.html":"https://ignacioalvmar.com/markdown/","/nmp/":"https://ignacioalvmar.com/non-menu-page/","/nmp.html":"https://ignacioalvmar.com/non-menu-page/","/cv/":"https://ignacioalvmar.com/resume/","/wordpress/blog-posts/":"https://ignacioalvmar.com/year-archive/"}
 Jupyter notebook markdown generator
# Jupyter notebook markdown generator These .ipynb files are Jupyter notebook files that convert a TSV containing structured data about talks (`talks.tsv`) or presentations (`presentations.tsv`) into individual markdown files that will be properly formatted for the academicpages template. The notebooks contain a lot of documentation about the process. The .py files are pure python that do the same things if they are executed in a terminal, they just don't have pretty documentation.
 
 {% if page.xsl %} {% endif %} {% assign collections = site.collections | where_exp:'collection','collection.output != false' %}{% for collection in collections %}{% assign docs = collection.docs | where_exp:'doc','doc.sitemap != false' %}{% for doc in docs %} {{ doc.url | replace:'/index.html','/' | absolute_url | xml_escape }} {% if doc.last_modified_at or doc.date %}{{ doc.last_modified_at | default: doc.date | date_to_xmlschema }} {% endif %} {% endfor %}{% endfor %}{% assign pages = site.html_pages | where_exp:'doc','doc.sitemap != false' | where_exp:'doc','doc.url != "/404.html"' %}{% for page in pages %} {{ page.url | replace:'/index.html','/' | absolute_url | xml_escape }} {% if page.last_modified_at %}{{ page.last_modified_at | date_to_xmlschema }} {% endif %} {% endfor %}{% assign static_files = page.static_files | where_exp:'page','page.sitemap != false' | where_exp:'page','page.name != "404.html"' %}{% for file in static_files %} {{ file.path | replace:'/index.html','/' | absolute_url | xml_escape }} {{ file.modified_time | date_to_xmlschema }}  {% endfor %}
 
Sitemap: {{ "sitemap.xml" | absolute_url }}
 
{% if page.xsl %}{% endif %}<feed xmlns="http://www.w3.org/2005/Atom" {% if site.lang %}xml:lang="{{ site.lang }}"{% endif %}>Jekyll<link href="{{ '/' | absolute_url }}" rel="alternate" type="text/html" {% if site.lang %}hreflang="{{ site.lang }}" {% endif %}/>{{ site.time | date_to_xmlschema }}{{ page.url | absolute_url | xml_escape }}{% assign title = site.title | default: site.name %}{% if page.collection != "posts" %}{% assign collection = page.collection | capitalize %}{% assign title = title | append: " | " | append: collection %}{% endif %}{% if page.category %}{% assign category = page.category | capitalize %}{% assign title = title | append: " | " | append: category %}{% endif %}{% if title %}{{ title | smartify | xml_escape }}{% endif %}{% if site.description %}{{ site.description | xml_escape }}{% endif %}{% if site.author %}{{ site.author.name | default: site.author | xml_escape }}{% if site.author.email %}{{ site.author.email | xml_escape }}{% endif %}{% if site.author.uri %}{{ site.author.uri | xml_escape }}{% endif %}{% endif %}{% if page.tags %}{% assign posts = site.tags[page.tags] %}{% else %}{% assign posts = site[page.collection] %}{% endif %}{% if page.category %}{% assign posts = posts | where: "categories", page.category %}{% endif %}{% unless site.show_drafts %}{% assign posts = posts | where_exp: "post", "post.draft != true" %}{% endunless %}{% assign posts = posts | sort: "date" | reverse %}{% assign posts_limit = site.feed.posts_limit | default: 10 %}{% for post in posts limit: posts_limit %}<entry{% if post.lang %}{{" "}}xml:lang="{{ post.lang }}"{% endif %}>{% assign post_title = post.title | smartify | strip_html | normalize_whitespace | xml_escape %}{{ post_title }}{{ post.date | date_to_xmlschema }}{{ post.last_modified_at | default: post.date | date_to_xmlschema }}{{ post.id | absolute_url | xml_escape }}{% assign excerpt_only = post.feed.excerpt_only | default: site.feed.excerpt_only %}{% unless excerpt_only %}<![CDATA[{{ post.content | strip }}]]>{% endunless %}{% assign post_author = post.author | default: post.authors[0] | default: site.author %}{% assign post_author = site.data.authors[post_author] | default: post_author %}{% assign post_author_email = post_author.email | default: nil %}{% assign post_author_uri = post_author.uri | default: nil %}{% assign post_author_name = post_author.name | default: post_author %}{{ post_author_name | default: "" | xml_escape }}{% if post_author_email %}{{ post_author_email | xml_escape }}{% endif %}{% if post_author_uri %}{{ post_author_uri | xml_escape }}{% endif %}{% if post.category %}{% elsif post.categories %}{% for category in post.categories %}{% endfor %}{% endif %}{% for tag in post.tags %}{% endfor %}{% assign post_summary = post.description | default: post.excerpt %}{% if post_summary and post_summary != empty %}<![CDATA[{{ post_summary | strip_html | normalize_whitespace }}]]>
{% endif %}{% assign post_image = post.image.path | default: post.image %}{% if post_image %}{% unless post_image contains "://" %}{% assign post_image = post_image | absolute_url %}{% endunless %}{% endif %}</entry>{% endfor %}</feed>

Posts by Category

Posts by Collection

No venue listed (2024)

PDF

BibTeX

@article{liuyang2024misbehaviordete,
  author    = {Liuyang Yang et al.},
  title     = {Misbehavior detection using data consistency checks for collective perception messages},
  journal  = {No venue listed},
  year      = {2024}
}

portfolio

Portfolio item number 1

This is an item in your portfolio. It can be have images or nice text. If you name the file .md, it will be parsed as markdown. If you name the file .html, it will be parsed as HTML.

Portfolio item number 2

This is an item in your portfolio. It can be have images or nice text. If you name the file .md, it will be parsed as markdown. If you name the file .html, it will be parsed as HTML.

publications

Voice interfaced vehicle user help

Published in Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 2010

Abstract

Manuals were designed to provide support and information about the usage and maintenance of the vehicle. In many cases user’s manuals are the driver’s only guidance. However, lack of clarity and efficiency of manuals lead to user dissatisfaction. In vehicles this problem is even more crucial given that driving a motor vehicle is, for many people, the most complex and potentially dangerous task they will perform during their lifetime. In this paper we present a voice interfaced driver manual that can potentially fix the deficiencies of its alternatives. In addition we aim to provide a case for the integration of such technology in a vehicle to reduce driver distraction, increase driver satisfaction, and manual usability, while also benefiting Original Equipment Manufacturers (OEMs) in lowering costs and reducing the documentation process.

Key Contributions

Voice-interfaced user help system design
Conversational query methodology
Dynamic voice user interface architecture
Prototype development and database design
Context-aware system and architecture vision

Download paper

Citation

@article{ignacio2010voiceinterfaced, author = {Ignacio Alvarez et al.}, title = {Voice interfaced vehicle user help}, journal = {Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, year = {2010} }

Alvarez, I., et al. (2010). Voice interfaced vehicle user help. Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

Are educational video games all they are cracked up to be

Published in ‘International Journal of Learning Technology’, 2011

Abstract

This paper investigates the benefits of learning from educational video games compared to learning by reading from a text document. The participants were exposed to Lewis and Clark expedition via a video game or text document. During the learning task, playing the game or reading, participants wore a Brain Computer Interface (BCI) device to gather their level of engagement. After the learning sessions, post-experiment questionnaires were used to assess the amount of information retained after each session. The results of this study suggests that the educational video games might not be significantly engaging, and also that learning by reading a handout may be better for retaining information. Furthermore, this paper briefly discusses the BCI device, and how it can be used to measure engagement of the participants.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

Download paper

Citation

@inproceedings{andujar2011educational, title={Are educational video games all they're cracked up to be?: A physiological approach for measuring engagement in educational video games vs. conventional learning techniques}, author={Andujar, Marvin and Ekandem, Josh and Alvarez, Ignacio and James, Melva and Gilbert, Juan}, booktitle={eLearn: World Conference on EdTech}, pages={539--544}, year={2011}, organization={Association for the Advancement of Computing in Education (AACE)} }

Andujar, M., Ekandem, J., Alvarez, I., James, M., & Gilbert, J. (2011). Are educational video games all they're cracked up to be?: A physiological approach for measuring engagement in educational video games vs. conventional learning techniques. eLearn: World Conference on EdTech, 539-544.

Autonui a workshop on automotive natural user interfaces

Published in ‘Proceedings of AutomotiveUI 2011’, ACM 2011

Abstract

Natural user interfaces by means of gesture and speech interaction have become a hot topic in research as well as already for real products. Most use cases currently center around consumer electronics devices like smart phones, TV sets, gaming, or other large screens like tabletops. Motivated by the latest results in those areas, our vision is to apply natural user interfaces like gesture and speech interaction to the automotive domain as well. This integration might on one hand reduce driver distraction in certain cases and on the other hand allow to design new experiences for operating infotainment and entertainment systems. The goal of this workshop is to explore the design space of natural multi-modal automotive user interfaces. We would like to analyze where and how new interaction techniques can be integrated into the car.

Key Contributions

Natural User Interfaces in Automotive
Workshop organization and community building

Download paper

Citation

@inproceedings{pfleging2011autonui, title={Autonui: a workshop on automotive natural user interfaces}, author={Pfleging, Bastian and Schmidt, Albrecht and D{"o}ring, Tanja and Knobel, Martin}, booktitle={Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages={207--209}, year={2011} }

Pfleging, B., Schmidt, A., Döring, T., & Knobel, M. (2011). Autonui: a workshop on automotive natural user interfaces. Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 207-209.

Designing driver centric natural voice user interfaces

Published in Adjunct Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 2011

Abstract

The current growth of automotive electronics aims toextend vehicle functionality and information access. Thispaper explores the application of Natural Voice UserInterfaces as a preferred interaction modality with in-vehicle technologies to lower driver distraction effects andimprove the user experience. The benefits and risks ofnatural speech interactions are evaluated in order topropose a driver-centric design guideline based on previousresearch. The paper concludes that driving scenarios canprofit considerably from systems that apply natural speechinterfaces to allow the driver to access information.

Key Contributions

Natural Voice Interaction
Voice-interfaced user help system design
Conversational Dialogue Managment
Context-aware system and architecture vision

Download paper

Citation

@inproceedings{alvarez2011designing, title={Designing driver-centric natural voice user interfaces}, author={Alvarez, Ignacio and Martin, Aqueasha and Dunbar, Jerone and Taiber, Joachim and Wilson, Dale-Marie and Gilbert, Juan E}, booktitle={Adjunct Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications. online}, pages={156--159}, year={2011} }

Alvarez, Ignacio and Martin, Aqueasha and Dunbar, Jerone and Taiber, Joachim and Wilson, Dale-Marie and Gilbert, Juan E (2011). Designing driver centric natural voice user interfaces. Adjunct Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications. online.

iHelp, the Ubiquitous Vehicle User Help

Published in Proceedings of Interact 2011, 2011

Abstract

Voice-interfaced, in-vehicle assistance includes receiving a Voice-based query from a user in the vehicle, and then determining at least one of a user emotional state, user expertise level and speech recognition confidence level associated with the Voice-based query. A text-based query may then be derived from the Voice-based query, and used to search a help database for answers corresponding to the Voice-based query. At least one response is then provided to the user in the form of Voice-based assistance in accordance with at least one of the user emotional state, user expertise level and speech recognition confidence level.

Key Contributions

Voice-interfaced user help
Conversational dialogue management
Prototype development

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Citation

@inproceedings{alvarez2011ihelp, title={iHelp, the Ubiquitous Vehicle User Help}, author={Alvarez, Ignacio and Fischer, Hans-Peter}, booktitle={Proceedings of Interact}, pages={5--9}, year={2011} }

Alvarez, I. (2011). . Proceedings of Interact.

AutoNUI: 2nd Workshop on Automotive Natural User Interfaces

Published in ‘Adjunct Proceedings of the 4th International Conference on Automotive User Interfaces, ACM 2012

Abstract

Natural user interfaces—generally based on gesture and speech interaction—are an increasingly hot topic in research and are already being applied in a multitude of commercial products. Most use cases currently involve consumer electronics devices like smart phones, tablets, TV sets, game consoles, or large-screen tabletop computers.Motivated by the latest results in those areas, our vision is to apply natural user interfaces, for example gesture and conversational speech interaction, to the automotive domain as well. This integration might on one hand reduce driver distraction in certain cases and on the other hand might allow the design of new user experiences for infotainment and entertainment systems. The goal of this workshop is to explore the design space of natural multi-modal automotive user interfaces and to continue the fruitful discussions held at the 1st Workshop on Automotive Natural User Interfaces from AutomotiveUI ’11 in Salzburg, Austria. We would like to analyze where and how new interaction techniques can be integrated into the car.

Key Contributions

Workshop organization and community building

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Citation

@inproceedings{pflegling2012autonui, title={AutoNUI: 2nd Workshop on Automotive Natural User Interfaces}, author={Pflegling, Bastian and D{"o}ring, Tanja and Alvarez, Ignacio and Kranz, Matthias and Weinberg, Garrett and Healey, Jennifer}, booktitle={International Conference on Automotive User Interfaces and Interactive Vehicular Applications: 17/10/2012-19/10/2012}, year={2012} }

Pflegling, Bastian and D{"o (2012). AutoNUI: 2nd Workshop on Automotive Natural User Interfaces. International Conference on Automotive User Interfaces and Interactive Vehicular Applications: 17/10/2012-19/10/2012.

Contribution to the development of intelligent conversational assistants in automotive environments

Published in EHU Scientific Production Portal, EHU 2012

Abstract

This thesis presents the design and development of the Conversational Automotive Assistant, Voice User Help, a conversational system based on the question-answer paradigm and designed to consult vehicle documentation while driving. This work compiles research in the fields of technical documentation, information retrieval, natural language processing, vehicle user interface design, user experience, and affective computing with the goal of creating an adaptive and dynamic assistant that modifies its conversational behavior depending on the user’s emotional state.

Key Contributions

Conversational Asistant for In-cabin Automotive Applications
Information Retrieval Algorithms
Human-Computer Interaction Voice Interfaces
Automotive Context-aware Affective Computing

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Citation

@phdthesis{martinez2012contribution, title={Contribution to the development of intelligent conversational assistants in automotive environments}, author={Mart{\'\i}nez, Ignacio Javier {\'A}lvarez}, year={2012}, school={Universidad del Pa{\'\i}s Vasco-Euskal Herriko Unibertsitatea} }

Alvarez, I. (2012). . .

Emotional Adaptive Vehicle User Interfaces, Moderating negative effects of failed technology interactions while driving

Published in ‘Adjunct Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI 2012)’, 2012

Abstract

Automotive Natural User Interfaces have the potential to increase user experience providing intuitive interactions for drivers. However, in the complex setting of a driving vehicle, failed interactions with in-vehicle technology can lead to frustration and put drivers in a dangerous situation. This paper evaluates the possibility of applying emotion recognition to vehicular spoken dialogue systems in order to adapt the dialog strategies, in error recovery scenarios. An emotional taxonomy is developed for the interactions with a conversational vehicular application, the Voice User Help. The positive results of the performance of VUH emotion recognizer support the creation of real-time classification of the user emotional state, which serves as basis to emotional reappraisal dialog strategies that mitigate negative effects on the driver’s cognitive load and driver performance.

Key Contributions

Developed the Voice User Help system
Created a VUH-specific emotional taxonomy
Built a real-time emotion recognizer
Proposed adaptive emotional dialog strategies
Used emotion for error recovery

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Citation

@inproceedings{alvarez2012emotional, title={Emotional Adaptive Vehicle User Interfaces: moderating negative effects of failed technology interactions while driving}, author={Alvarez, Ignacio and Lopez-de Ipi{\~n}a, Karmele and Daily, Shaundra B and Gilbert, Juan E}, booktitle={Proceedings of Workshop of Automotive Natural Interfaces, together with International Conference on Automotive User Interfaces}, pages={57--60}, year={2012} }

Alvarez, Ignacio and Lopez-de Ipi{\~n (2012). Emotional Adaptive Vehicle User Interfaces, Moderating negative effects of failed technology interactions while driving. Proceedings of Workshop of Automotive Natural Interfaces, together with International Conference on Automotive User Interfaces.

Evaluating the ergonomics of BCI devices for research and experimentation

Published in Journal of Ergonomics, Taylor & Francis Online 2012

Abstract

The use of brain computer interface (BCI) devices in research and applications has exploded in recent years. Applications such as lie detectors that use functional magnetic resonance imaging (fMRI) to video games controlled using electroencephalography (EEG) are currently in use. These developments, coupled with the emergence of inexpensive commercial BCI headsets, such as the Emotiv EPOC ( http://emotiv.com/index.php) and the Neurosky MindWave, have also highlighted the need of performing basic ergonomics research since such devices have usability issues, such as comfort during prolonged use, and reduced performance for individuals with common physical attributes, such as long or coarse hair. This paper examines the feasibility of using consumer BCIs in scientific research. In particular, we compare user comfort, experiment preparation time, signal reliability and ease of use in light of individual differences among subjects for two commercially available hardware devices, the Emotiv EPOC and the Neurosky MindWave. Based on these results, we suggest some basic considerations for selecting a commercial BCI for research and experimentation.

Statement of Relevance: Despite increased usage, few studies have examined the usability of commercial BCI hardware. This study assesses usability and experimentation factors of two commercial BCI models, for the purpose of creating basic guidelines for increased usability. Finding that more sensors can be less comfortable and accurate than devices with fewer sensors.

Key Contributions

BCI evaluation
BCI Signal Limitation Modelling
Practical Use recommendations

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Citation

@article{ekandem2012evaluating, title={Evaluating the ergonomics of BCI devices for research and experimentation}, author={Ekandem, Joshua I and Davis, Timothy A and Alvarez, Ignacio and James, Melva T and Gilbert, Juan E}, journal={Ergonomics}, volume={55}, number={5}, pages={592--598}, year={2012}, publisher={Taylor \& Francis} }

Alvarez, I. (2012). . Ergonomics.

The voice user help, a smart vehicle assistant for the elderly

Published in Proceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence, Springer 2012

Abstract

The rapid advancement of vehicular technologies has resulted in an exponential increase of new vehicle functions road assistance and connected vehicles services. Vehicle manuals are designed to provide support and information about the use and maintenance of these features. However, current vehicle user manuals do not allow looking for information under driving conditions. This paper presents the Voice User Help, a smart voice-operated system that utilizes natural language understanding and emotional adaptive interfaces to assist drivers when looking for vehicle information with minimal effect on their driving performance. Additionally, the system presents an opportunity for elder drivers to reduce the learning curve of new in-vehicle technologies and improve efficiency. Results on user acceptance of the Voice User Help, as well as cognitive load and driver distraction effects generated during a simulated drive indicate that the Voice User help is an extremely desirable feature and potentially safe application since it did not significantly decrement driving performance. Furthermore preliminary results on adaptive voice interfaces using emotion recognition indicate that personalization of the interaction will be able to palliate possible negative effects that happen during system error recovery.

Key Contributions

Developed voice-operated vehicle help system
Aimed to assist elderly drivers
Evaluated driver distraction and usability
Integrated emotion recognition for adaptation
Created a VUH-specific emotional taxonomy

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BibTeX

Driver in the loop: Best practices in automotive sensing and feedback mechanisms

Published in ‘Automotive user interfaces: creating interactive experiences in the car’, 2017

Abstract

Given the rapid advancement of technologies in the automotive domain, driver–vehicle interaction has recently become more and more complicated. The amount of research applied to the vehicle cockpit is increasing, with the advent of (highly) automated driving, as the range of interaction that is possible in a driving vehicle expands. However, as opportunities increase, so does the number of challenges that automotive user experience designers and researchers will face. This chapter focuses on the instrumentation of sensing and displaying techniques and technologies to make better user experience while driving. In the driver–vehicle interaction loop, the vehicle can sense driver states, analyze, estimate, and model the data, and then display it through the appropriate channels for intervention purposes. To improve the interaction, a huge number of new/affordable sensing (EEG, fNIRS, IR imaging) and feedback (head-up displays, auditory feedback, tactile arrays, etc.) techniques have been introduced. However, little research has attempted to investigate this area in a systematic way. This chapter provides an overview of recent advances of input and output modalities to be used for timely, appropriate driver–vehicle interaction. After outlining relevant background, we provide information on the best-known practices for input and output modalities based on the exchange results from the workshop on practical experiences for measuring and modeling drivers and driver–vehicle interactions at AutomotiveUI 2015. This chapter can help answer research questions on how to instrument a driving simulator or realistic study to gather data and how to place interaction outputs to enable appropriate driver interactions.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

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Citation

@incollection{riener2017driver, title={Driver in the loop: Best practices in automotive sensing and feedback mechanisms}, author={Riener, Andreas and Jeon, Myounghoon and Alvarez, Ignacio and Frison, Anna K}, booktitle={Automotive user interfaces: creating interactive experiences in the car}, pages={295--323}, year={2017}, publisher={Springer} }

Riener, Andreas and Jeon, Myounghoon and Alvarez, Ignacio and Frison, Anna K (2017). Driver in the loop: Best practices in automotive sensing and feedback mechanisms. Automotive user interfaces: creating interactive experiences in the car.

Skyline: A Platform Towards Scalable UX-Centric In-Vehicle HMI Development

Published in ‘International Journal of Mobile Human Computer Interaction (IJMHCI)’, 2017

Abstract

This paper describes the research and development process of an in-vehicle user experience using Skyline, an automotive prototyping platform created in Intel Labs to empower interaction designers and user experience researches to rapidly and iteratively develop and test in-vehicle user experience concepts. The paper describes the hardware and software components of Skyline in depth and how to configure them to suit individual researcher needs. The paper also presents a case study to exemplify the design making process that Skyline enables. From ideation to use-case creation, prototyping and validation through user assessment, the paper showcases the benefits of capturing early qualitative user feedback as support for rapid prototyping walking through a study titled Agency vs. Control and the associated interactions inside the cockpit. Ten defined use-cases are developed and integrated into a hero scenario in Skyline. High fidelity HMI concepts are tested and validated over the course of six months with feedback from a total of fifty users.

Key Contributions

Automotive HMI Prototyping
Rapid HCI User Interface Design
Evaluation Methods

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Citation

@article{alvarez2017skyline, title={Skyline: A Platform Towards Scalable UX-Centric In-Vehicle HMI Development}, author={Alvarez, Ignacio and Rumbel, Laura}, journal={International Journal of Mobile Human Computer Interaction (IJMHCI)}, volume={9}, number={3}, pages={34--53}, year={2017}, publisher={IGI Global Scientific Publishing} }

Alvarez, Ignacio and Rumbel, Laura (2017). Skyline: A Platform Towards Scalable UX-Centric In-Vehicle HMI Development. International Journal of Mobile Human Computer Interaction (IJMHCI).

Socializing under the influence of distracted driving: a study of the effects of in-vehicle and outside-of-the-vehicle communication while driving

Published in ‘Springer Nature Advances in Human Aspects of Transportation’, Springer 2017

Abstract

Advancements of in-vehicle technologies and the development of mobile applications that keep a driver connected in a driving environment have caused an increasingly dangerous safety concern. Distracted driving has gained the attention of legislators and governments globally. Countries have constituted bans that partially or fully forbid drivers from using gadgets while driving, especially hindering out-of-the-vehicle communications. This paper introduces Voiceing™, a voice-activated application meant to improve social communications in the car, serving as a safe alternative to distracted driving. Other modalities of interaction such as texting, in-vehicle conversations and outside-of-the-vehicle conversation have been measured and compared with Voiceing™ investigating effects on driver’s performance, cognitive load and user acceptance. Results from this study suggest that Voiceing™ is a safer alternative than in-vehicle interactions with humans. Results also show that natural speech interaction of in-vehicle applications and the inclusion of context awareness help improve driving performance while interacting with a vehicle system.

Key Contributions

Introduces Voiceing™ a voice-activated application
Compares various in-car communication methods
Measures performance, cognitive load, acceptance
Voiceing™ is safer than human interactions
Natural speech improves driving performance

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Citation

@inproceedings{alnizami2016socializing, title={Socializing under the influence of distracted driving: a study of the effects of in-vehicle and outside-of-the-vehicle communication while driving}, author={Alnizami, Hanan and Alvarez, Ignacio and Gilbert, Juan E}, booktitle={Advances in Human Aspects of Transportation: Proceedings of the AHFE 2016 International Conference on Human Factors in Transportation, July 27-31, 2016, Walt Disney World{\textregistered}, Florida, USA}, pages={243--255}, year={2016}, organization={Springer} }

Alnizami, Hanan and Alvarez, Ignacio and Gilbert, Juan E (2016). Socializing under the influence of distracted driving: a study of the effects of in-vehicle and outside-of-the-vehicle communication while driving. Advances in Human Aspects of Transportation: Proceedings of the AHFE 2016 International Conference on Human Factors in Transportation, July 27-31, 2016, Walt Disney World{\textregistered.

The insight–prototype–product cycle best practices and processes to iteratively advance in-vehicle interactive experiences development

Published in Automotive User Interfaces: Creating Interactive Experiences in the Car, Springer Book, Springer 2017

Abstract

In-vehicle experiences are made up mainly of mundane small moments, repeated practices, and taken-for-granted decisions that make up daily experiences in and around private passenger vehicles. Understanding what those experiences are for drivers around the world presents an opportunity for designing novel interactive experiences, technologies, and user interfaces for vehicles. In this chapter, we present a set of tools, methodologies, and practices that will help reader create a holistic design space for future mobility. Transitioning between ethnography, insights, prototyping, experience design, and requirements decomposition is a challenging task even for experienced UX professionals. This chapter provides guidance in this matter with practical examples.

Key Contributions

Ethnographic research methods for Automotive System Development
Rapid prototyping methods for Automotive Functions
Experience Design mapping to product specifications
Product Development evaluation and validation
Product Success metrics

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Citation

@incollection{alvarez2017insight, title={The insight--prototype--product cycle best practices and processes to iteratively advance in-vehicle interactive experiences development}, author={Alvarez, Ignacio and Jordan, Adam and Knopf, Juliana and LeBlanc, Darrell and Rumbel, Laura and Zafiroglu, Alexandra}, booktitle={Automotive User Interfaces: Creating Interactive Experiences in the Car}, pages={377--400}, year={2017}, publisher={Springer} }

Alvarez, I. (2017). . Automotive User Interfaces: Creating Interactive Experiences in the Car.

Towards Adaptive Ambient In-Vehicle Displays and Interactions: Insights and Design Guidelines from the 2015 AutomotiveUI Dedicated Workshop

Published in ‘Automotive User Interfaces: Creating Interactive Experiences in the Car’, ACM 2017

Abstract

Informing a driver of a vehicle’s changing state and environment is a major challenge that grows with the introduction of in-vehicle assistant and infotainment systems. Even in the age of automation, the human will need to be in the loop for monitoring, taking over control, or making decisions. In these cases, poorly designed systems could lead to needless attentional demands imparted on the driver, taking it away from the primary driving task. Existing systems are offering simple and often unspecific alerts, leaving the human with the demanding task of identifying, localizing, and understanding the problem. Ideally, such systems should communicate information in a way that conveys its relevance and urgency. Specifically, information useful to promote driver safety should be conveyed as effective calls for action, while information not pertaining to safety (therefore less important) should be conveyed in ways that do not jeopardize driver attention. Adaptive ambient displays and peripheral interactions have the potential to provide superior solutions and could serve to unobtrusively present information, to shift the driver’s attention according to changing task demands, or enable a driver to react without losing the focus on the primary task. In order to build a common understanding across researchers and practitioners from different fields, we held a Workshop on Adaptive Ambient In-Vehicle Displays and Interactions at the AutomotiveUI`15 conference. In this chapter, we discuss the outcomes of this workshop, provide examples of possible applications now or in the future and conclude with challenges in developing or using adaptive ambient interactions.

Key Contributions

Workshop organization and community building
Human-computer interaction design

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Citation

@Inbook{Löcken2017, author={Löcken, Andreas and Sadeghian Borojeni, Shadan and Müller, Heiko and Gable, Thomas M. and Triberti, Stefano and Diels, Cyriel and Glatz, Christiane and Alvarez, Ignacio and Chuang, Lewis and Boll, Susanne}, editor={Meixner, Gerrit and Müller, Christian}, title={Towards Adaptive Ambient In-Vehicle Displays and Interactions: Insights and Design Guidelines from the 2015 AutomotiveUI Dedicated Workshop}, booktitle={Automotive User Interfaces: Creating Interactive Experiences in the Car}, year={2017}, publisher={Springer International Publishing}, address={Cham}, pages={325--348}, isbn={978-3-319-49448-7}, doi={10.1007/978-3-319-49448-7_12}, url={https://doi.org/10.1007/978-3-319-49448-7_12} }

Löcken, A., Sadeghian Borojeni, S., Müller, H., Gable, T. M., Triberti, S., Diels, C., Glatz, C., Alvarez, I., Chuang, L., & Boll, S. (2017). Towards Adaptive Ambient In-Vehicle Displays and Interactions: Insights and Design Guidelines from the 2015 AutomotiveUI Dedicated Workshop. In G. Meixner & C. Müller (Eds.), Automotive User Interfaces: Creating Interactive Experiences in the Car (pp. 325-348). Springer International Publishing.

Workshop on user-centered design for automated driving systems

Published in Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, ACM 2017

Abstract

Automated driving systems (ADS) are mainly regarded from an innovation and technology-centered perspective. In academia, as well as in industry, there is a concentration on technical issues to maintain competitiveness while aspects like acceptance, trust and user experience are widely under-researched. However, the “human factor” is critical for a comprehensive establishment of ADS technology on the market. We believe that there is a need to focus on a user-centered design (UCD) perspective to bring ADS innovation to a next level and to achieve a wide acceptance in society. In this workshop we want to discuss special requirements of UCD applied to ADS, to address challenges and opportunities and to reveal new research fields for future work.

Key Contributions

Proposes migration-tolerant ATC display concept
Advocates for stepwise functionality introduction
Compares small versus large integration steps
Measures usability, learnability, and acceptance

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Citation

@inproceedings{10.1145/3131726.3131734, author = {Frison, Anna-Katharina and Pfleging, Bastian and Riener, Andreas and Jeon, Myounghoon Philart and Alvarez, Ignacio and Ju, Wendy}, title = {Workshop on User-Centered Design for Automated Driving Systems}, year = {2017}, isbn = {9781450351515}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3131726.3131734}, doi = {10.1145/3131726.3131734}, booktitle = {Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications Adjunct}, pages = {22–27}, numpages = {6}, keywords = {User-centered design, Automotive user interfaces, Automated driving systems}, location = {Oldenburg, Germany}, series = {AutomotiveUI '17} }

Alvarez, I. (2017). . Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications Adjunct.

An international survey on automated and electric vehicles: Austria, Germany, South Korea, and USA

Published in ‘Springer Natural Digital Human Modelling’, Springer 2018

Abstract

As development of automated vehicles and adoption of electric vehicles continue to grow, there is an increasing interest in the public opinions on these technologies. We conducted an international online survey to gather information about people’s hopes and concerns for automated and electric vehicles from a total of 866 people from four countries – Austria, Germany, South Korea, and USA. Results revealed some differences across countries in the perceptions of automated and electric vehicles. However, differences between the same countries have shrunk compared to our previous survey completed in 2012. Results are discussed with limitations and future work.

Key Contributions

Automated driving system development
Electric Vehicles
International Trends

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Citation

@inproceedings{jeon2018international, title={An international survey on automated and electric vehicles: Austria, Germany, South Korea, and USA}, author={Jeon, Myounghoon and Riener, Andreas and Sterkenburg, Jason and Lee, Ju-Hwan and Walker, Bruce N and Alvarez, Ignacio}, booktitle={International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management}, pages={579--587}, year={2018}, organization={Springer} }

Jeon, Myounghoon and Riener, Andreas and Sterkenburg, Jason and Lee, Ju-Hwan and Walker, Bruce N and Alvarez, Ignacio (2018). An international survey on automated and electric vehicles: Austria, Germany, South Korea, and USA. International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management.

ARV 2018: 2nd Workshop on Augmented Reality for Intelligent Vehicles

Published in ‘Adjunct Proceedings of the 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’18)’, ACM 2018

Abstract

Augmented reality (AR) has the potential to improve road safety, support more immersive (non-) driving related activities, and finally enhance driving experience. AR may also be the enabling technology to help on the transition towards automated driving. However, augmented reality still faces a number of technical challenges when applied in vehicles, and also several human factors issues need to be solved. In this workshop, we will discuss potential and constraints as well as impact, role, and adequacy of AR in driving applications. The primary goal of this workshop is to define a research agenda for the use of AR in intelligent vehicles within the next 3 to 5 years.

Key Contributions

Workshop organization and community building
Defines a 3-5 year collaborative research agenda
Identifies four key AR problem fields
Discusses practical usage of AR devices
Outlines research challenges and hypotheses

In-car emotion detection and regulation have become an emerging and important branch of research within the automotive domain. Different emotional states can greatly influence human driving performance and user experience both in manual and automated driving conditions. The monitoring and regulation of relevant emotional states is therefore important to avoid critical driving scenarios with the human driver being in charge, and to ensure comfort and acceptance in autonomous driving. In this workshop we want to discuss the empathic user interface research to address challenges and opportunities and to reveal new research directions for future work. This workshop provides a forum for exchange and discussion on empathic user interfaces, including methods for emotion recognition and regulation, empathic automotive human-machine interaction design, user evaluation and measurements, and subsequent improvement of autonomous driving experience.

Key Contributions

Discusses empathic in-car user interfaces
Identifies critical scenarios and emotions
Explores emotion recognition and regulation methods
Uses 4mat system for workshop structure
Creates roadmap for future research

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Citation

@inproceedings{10.1145/3239092.3239098, author = {Bosch, Esther and Oehl, Michael and Jeon, Myounghoon and Alvarez, Ignacio and Healey, Jennifer and Ju, Wendy and Jallais, Christophe}, title = {Emotional GaRage: A Workshop on In-Car Emotion Recognition and Regulation}, year = {2018}, isbn = {9781450359474}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3239092.3239098}, doi = {10.1145/3239092.3239098}, booktitle = {Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages = {44–49}, numpages = {6}, keywords = {user acceptance, empathic vehicles, emotion recognition and regulation, Driver state assessment}, location = {Toronto, ON, Canada}, series = {AutomotiveUI '18} }

Bosch, Esther and Oehl, Michael and Jeon, Myounghoon and Alvarez, Ignacio and Healey, Jennifer and Ju, Wendy and Jallais, Christophe (2018). Emotional GaRage: A workshop on in-car emotion recognition and regulation. Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

“Peace of Mind”, An Experiential Safety Framework for Automated Driving Technology Interactions

Published in Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), IEEE 2018

Abstract

Current automated driving systems assume drivers continuously monitor the vehicle. Meanwhile, fully automated vehicles aim at not requiring human intervention for their safely operation. The industry is currently debating how these novel systems can be certified under functional safety standards. In this paper, we argue that the current safety picture is not comprehensive enough, since it alienates users. We propose experiential safety as a complement to existing functional safety and to develop a framework for experiential safety interactions between the user and automation in automated driving environments. To support the experiential safety design model, we provide an overview of the user-centered research on experiential automation safety, which includes results from online surveys, personal interviews, and gamified group workshops. We explore current user behaviors by focusing on what makes them feel safe as drivers and passengers, and how unexpected events and automation responses might impact their perception of safety. Among the highlighted results, we show how mismatched expectations and unexpected behaviors from autonomous vehicles can lead to frustration and compromised trust. We also show how automation feedback to the user can generate stress and anxiety if not properly configured and how a cooperative relationship between automation and the driver leads to more satisfying driving experiences. Finally, we present guidelines for the experiential safety to be applied by automotive engineers and designers in their development of automated driving technologies.

Key Contributions

Proposes an experiential safety concept for AVs
Develops a novel gamified workshop method
Presents an experiential safety framework
Defines user-lenses and machine-lenses guidelines
Finds collaborative feedback improves user emotion

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Citation

@INPROCEEDINGS{8569686, author={Strano, Marina and Novak, Fabricio and Walbert, Shelly and Palmeiro, Beatriz and Morales, Sonia and Alvarez, Ignacio}, booktitle={2018 21st International Conference on Intelligent Transportation Systems (ITSC)}, title={“Peace of Mind”, An Experiential Safety Framework for Automated Driving Technology Interactions}, year={2018}, volume={}, number={}, pages={53-59}, keywords={Safety;Automation;Interviews;Automobiles;Guidelines;Task analysis;Automated Driving;Experiential Safety;User experience design;design research;design theory;design science;methodology design;emerging technology}, doi={10.1109/ITSC.2018.8569686} }

Alvarez, I. (2018). . 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

Theo, take a right… uh… left: Conversational Route Negotiations with Autonomous Driving Assistants

Published in Adjunct Proceedings of the 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’18), ACM 2018

Abstract

We foresee conversational driver assistants playing a crucial role in automated driving interactions. In this video we present a study of user interactions with an in-vehicle agent, “Theo”, under SAE Level 4 automated driving. We use a remote Wizard-of-Oz setup where participants, sitting in a driving simulator, experience real-life video footage transmitted from a vehicle in the neighborhood and interact with Theo to instruct the vehicle where to go. We configured Theo to present 3 levels of conversational abilities (terse, verbose and helpful). We show the results of 9 participants tasked to negotiate destinations and route changes. Voice interaction was reported as preferred means of communication with Theo. There was a clear preference for talkative assistants which were perceived more responsive and intelligent. We highlight challenging interactions for users such as vehicle maneuvers in parking areas and specifying drop off points and interesting associations between the agent performance and the automated vehicle abilities.

Key Contributions

Automated driving system development
Pioneers a remote AI Wizard-of-Oz setup
Tests three conversational agent styles
Finds user preference for talkative agents
Identifies challenging user interaction scenarios
Links agent performance to perceived safety

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Citation

@inproceedings{10.1145/3239092.3267414, author = {Ekandem, Joshua E. and Alvarez, Ignacio and Rayburn, Cat and Johnson, Andrea}, title = {Theo, take a right ... uh ... left: Conversational Route Negotiations with Autonomous Driving Assistants}, year = {2018}, isbn = {9781450359474}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3239092.3267414}, doi = {10.1145/3239092.3267414}, booktitle = {Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages = {261–262}, numpages = {2}, keywords = {Advanced Driver Assistance Systems, Automated Driving, Conversational Dialogues, Natural Language Understanding, Route-Based Negotiation}, location = {Toronto, ON, Canada}, series = {AutomotiveUI '18} }

Ekandem, Joshua E. and Alvarez, Ignacio and Rayburn, Cat and Johnson, Andrea (2018). Theo, take a right... uh... left: Conversational Route Negotiations with Autonomous Driving Assistants. Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

Towards understanding emotional reactions of driver-passenger dyads in automated driving

Published in Proceedings of the 13th IEEE International Conference on Automatic Face & Gesture Recognition, IEEE 2018

Abstract

Automated driving has the potential to reduce the amount of fatal crashes, lighten the burden of commutes, and democratize mobility access to wider populations. But delegation of control to automation is not without issues. One of the foreseen drawbacks is that users might experience negative emotional reactions to unanticipated or unexplainable automated maneuvers. In this paper we present a novel method to induce targeted emotional reactions, frustration and startle, in simulated automated driving environments. We describe the data collection process for 17 driver - passenger dyads and discuss the data labelling method for generating reliable novel emotion datasets. This contribution is a foundational methodology towards expanding emotional understanding in automated vehicles, a critical skill for building long-term trusted experiences.

Key Contributions

Presents method to induce driver emotions
Studies emotional reactions of driver-passenger dyads
Develops a reliable emotion labeling process
Achieves expert consensus on emotion labels
Provides methodology for emotional understanding

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@INPROCEEDINGS{8373886, author={Alyuz, Nese and Aslan, Sinem and Healey, Jennifer and Alvarez, Ignacio J. and Esme, Asli Arslan}, booktitle={2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)}, title={Towards Understanding Emotional Reactions of Driver-Passenger Dyads in Automated Driving}, year={2018}, volume={}, number={}, pages={585-592}, keywords={Labeling;Vehicles;Data collection;Cameras;Automation;Roads;affective computing;automated driving;drive simulation;labeling;inter-rater agreement}, doi={10.1109/FG.2018.00093} }

Alvarez, I. (2018). . 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018).

Workshop on designing highly automated driving systems as radical innovation

Published in Adjunct Proceedings of the 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’18), ACM 2018

Abstract

Automated driving systems (ADS), especially in higher levels of automation, seem to be the new focus of innovation regarding future mobility. Technological achievements of traveling automation open up new challenges for road traffic. Existing automotive research focuses on problem solving and observational approaches including users and their imagination of the future of mobility to analyze acceptance and user experience of “incremental” (step-wised improved) innovations. On the other hand, “radical” (something new, enabled by technology or meaning change) innovations extensively increase product quality leaping over incremental innovation. This workshop aims to challenge the current research approaches to automated driving against “trying to improve sitting in a horse carriage” and discuss how we can design “radical” innovations for ADS beyond the “horse carriage”. Within this interactive workshop, we will utilize a design thinking approach to refocus on underlying problems that ADSs originally aim to solve and generate ideas for radical innovations.

Key Contributions

Critiques incremental innovation in ADS research
Promotes designing ADS as radical innovation
Applies a design thinking approach
Utilizes rapid prototyping for radical ideas
Refocuses on problems ADS should solve

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@inproceedings{10.1145/3239092.3239097, author = {Frison, Anna-Katharina and Riener, Andreas and Jeon, Myounghoon and Pfleging, Bastian and Alvarez, Ignacio}, title = {Workshop on Designing Highly Automated Driving Systems as Radical Innovation}, year = {2018}, isbn = {9781450359474}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3239092.3239097}, doi = {10.1145/3239092.3239097}, booktitle = {Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages = {37–43}, numpages = {7}, keywords = {Automated Driving Systems, Design Thinking, Innovation, SAE J3016, User-Centered Design}, location = {Toronto, ON, Canada}, series = {AutomotiveUI '18} }

Alvarez, I. (2018). . Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

2nd special session on solving the automated vehicle safety assurance challenge

Published in Proceedings of the 2019 International Conference on Intelligent Transportation Systems (ITSC), IEEE 2019

Abstract

The scientific knowledge and tools for the mass deployment of Automated Vehicles (AVs) are maturing rapidly, as evidenced by the wide deployment of AV test fleets worldwide. These vehicles have the potential to produce tremendous economic and societal benefits including greatly reduced traffic accidents, injuries, and congestion, and to make less expensive, more flexible and more productive transportation available to all. But one challenge looms above all others in the race to full vehicle automation; solving the AV Safety Assurance challenge. Automated transportation is not just a product, but an industry. And it is as an industry that we must together solve this challenge. We invite researchers, automakers, technology companies, and government regulators to come together to develop a holistic model to define and measure AV Safety. In this special session we present papers that provide contributions to the definition, applicability and standardization of AV safety assurance, including methods for the development of metrics, benchmarks and evangelisation of AV Safety Assurances to users and the public. This session continues the open discussion started in 2018 ITSC with the goal of making AV Safety a reality. The session will consist of high quality paper presentations as well as a panel discussion with some of the most relevant figures of the AV industry.

Key Contributions

Common AV safety definition
Fosters collaboration between industry and academia
Seeks to specify AV safety standards
Gathers a universal view on safety

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@inproceedings{elli20192nd, title={2nd special session on solving the automated vehicle safety assurance challenge}, author={Elli, Maria Soledad and Alvarez, Ignacio and Ota, Jeffrey and Weast, Jack and Maurer, Markus}, booktitle={International Conference on Intelligent Transportation Systems (ITSC)}, year={2019} }

Alvarez, I. (2019). . International Conference on Intelligent Transportation Systems (ITSC).

Design of a misbehavior detection system for objects based shared perception V2X applications

Published in ‘IEEE Intelligent Transportation Systems Conference’, IEEE 2019

Abstract

The recent trend of integrating vehicular communications with advanced sensors installed on vehicles, enables Connected and Autonomous Vehicles (CAVs) to share their own driving information as well as perception information, such as a list of perceived objects (e.g., dynamic obstacles such as vehicles, pedestrians, and cyclists, and static obstacles). This has the potential to improve driving safety by expanding collective perception of vehicles. However, adversaries may also populate false information to other Connected Vehicles (CVs) via Vehicle-to-Vehicle (V2V) communications. This paper investigates the security aspects of mixed deployment of CAVs, CVs and legacy vehicles, and in particular with regards to misbehavior detection. We provide a generic design framework that is independent from the specific algorithms of the underlying perception system, and can be used to implement a practical Misbehavior Detection System (MDS). We analyze the MDS framework w.r.t. a ghost vehicle attack. While no computing system can be completely secure, we believe this work would help the industry to develop a practical MDS design within a common framework while allowing individual techniques to mature and evolve over time with future academic research.

Key Contributions

Proposes a generic misbehavior detection framework
Divides detection into local/collaborative components
Categorizes four levels of anomaly detection
Defines Unobservability and Undecidability concepts
Defines conditions for successful attacks

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@inproceedings{8917066, author={Ambrosin, Moreno and Yang, Lily L and Liu, Xiruo and Sastry, Manoj R and Alvarez, Ignacio J}, booktitle={2019 IEEE Intelligent Transportation Systems Conference (ITSC)}, title={Design of a Misbehavior Detection System for Objects Based Shared Perception V2X Applications}, year={2019}, volume={}, number={}, pages={1165-1172}, keywords={Sensors;Autonomous vehicles;Roads;Safety;Space vehicles;Time measurement;Security}, doi={10.1109/ITSC.2019.8917066} }

Ambrosin, Moreno and Yang, Lily L and Liu, Xiruo and Sastry, Manoj R and Alvarez, Ignacio J (2019). Design of a misbehavior detection system for objects based shared perception V2X applications. 2019 IEEE Intelligent Transportation Systems Conference (ITSC).

Human-system cooperation in automated driving

Published in ‘International Journal of Human Computer Interaction’, Elsevier 2019

Abstract

To draw a comprehensive and cohesive understanding of human–vehicle cooperation in automated driving, a review is made on key studies in human–robot interaction and human factors. Throughout this article, insight is provided into how human drivers and vehicle systems interplay and influence each other. The limitations of technology-centered taxonomies of automation are discussed and the benefits of accounting for human agents are examined. The contributions of machine learning to automated driving and how critical models in human-system cooperation can inform the design of a more symbiotic relationship between driver and vehicle are investigated. Challenges in the human element to enable the safe introduction of road automation are also discussed. Particularly, the unintended consequences of vehicle automation on driver’s workload, situation awareness and trust are examined, and the social interactions between driver, vehicle, and other road users are investigated. This review will help professionals shape future directions for safer and more efficient and effective human–vehicle cooperation.

Key Contributions

Critiques technology-centered automation taxonomies
Promotes human-centered automation design.
Analyzes automation’s impact on drivers
Reviews machine learning for cooperation
Examines social human-vehicle interactions

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@article{biondi2019human, title={Human--vehicle cooperation in automated driving: A multidisciplinary review and appraisal}, author={Biondi, Francesco and Alvarez, Ignacio and Jeong, Kyeong-Ah}, journal={International Journal of Human--Computer Interaction}, volume={35}, number={11}, pages={932--946}, year={2019}, publisher={Taylor \& Francis} }

Biondi, Francesco and Alvarez, Ignacio and Jeong, Kyeong-Ah (2019). Human-system cooperation in automated driving. International Journal of Human--Computer Interaction.

Human–vehicle cooperation in automated driving: A multidisciplinary review and appraisal

Published in ‘International Journal of Human Computer Interaction’, Elsevier 2019

Abstract

To draw a comprehensive and cohesive understanding of human–vehicle cooperation in automated driving, a review is made on key studies in human–robot interaction and human factors. Throughout this article, insight is provided into how human drivers and vehicle systems interplay and influence each other. The limitations of technology-centered taxonomies of automation are discussed and the benefits of accounting for human agents are examined. The contributions of machine learning to automated driving and how critical models in human-system cooperation can inform the design of a more symbiotic relationship between driver and vehicle are investigated. Challenges in the human element to enable the safe introduction of road automation are also discussed. Particularly, the unintended consequences of vehicle automation on driver’s workload, situation awareness and trust are examined, and the social interactions between driver, vehicle, and other road users are investigated. This review will help professionals shape future directions for safer and more efficient and effective human–vehicle cooperation.

Key Contributions

Critiques technology-centered automation taxonomies
Promotes human-centered automation design.
Analyzes automation’s impact on drivers
Reviews machine learning for cooperation
Examines social human-vehicle interactions

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Citation

@article{biondi2019human, title={Human--vehicle cooperation in automated driving: A multidisciplinary review and appraisal}, author={Biondi, Francesco and Alvarez, Ignacio and Jeong, Kyeong-Ah}, journal={International Journal of Human--Computer Interaction}, volume={35}, number={11}, pages={932--946}, year={2019}, publisher={Taylor \& Francis} }

Biondi, Francesco and Alvarez, Ignacio and Jeong, Kyeong-Ah (2019). Human–vehicle cooperation in automated driving: A multidisciplinary review and appraisal. International Journal of Human--Computer Interaction.

Object-level perception sharing among connected vehicles

Published in Proceedings of 2019 IEEE Intelligent Transportation Systems Conference (ITSC), IEEE 2023

Abstract

Advances in vehicular communication technologies have made Connected Vehicles (CVs) a near-term reality. Compared to models where vehicles rely solely on their own perception to sense and make decision on the environment, CVs have the potential to allow information sharing among vehicles to improve sensing and decision making collectively. A first concrete step towards this goal is enabling vehicles to share perception-related information, to overcome limitations of their respective sensors (e.g., partial awareness due to occlusions). Sharing processed information vs raw sensor data has the advantage of reducing the amount of data to be transmitted, and the required computation burden at the receiver side.This paper proposes an approach to enable object-level sharing among vehicles. Following state-of-the-art object-level management techniques, we developed a two-layer architecture that handles object tracking and fusion from dynamic remote sources of information. We implemented our approach and showed that it can achieve realistic performance, and robustness both in terms of quality of information and computation.

Key Contributions

Proposes two-layer object fusion architecture
Defines connected vehicles as virtual sensors
Dynamically manages vehicles entering/leaving range
uses objects using distance-based clustering
Evaluates system accuracy and scalability

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@INPROCEEDINGS{8916837, author={Ambrosin, Moreno and Alvarez, Ignacio J and Buerkle, Cornelius and Yang, Lily L and Oboril, Fabian and Sastry, Manoj R and Sivanesan, Kathiravetpillai}, booktitle={2019 IEEE Intelligent Transportation Systems Conference (ITSC)}, title={Object-level Perception Sharing Among Connected Vehicles}, year={2019}, volume={}, number={}, pages={1566-1573}, keywords={Sensor fusion;Vehicle dynamics;Standards;Collaboration;Merging;Covariance matrices}, doi={10.1109/ITSC.2019.8916837} }

Alvarez, I. (2019). . 2019 IEEE Intelligent Transportation Systems Conference (ITSC).

The SKYNIVI experience: evoking startle and frustration in dyads and single drivers

Published in ‘Proceedings of the IEEE International Conference in Intelligent Transportations Systems 2019’, IEEE 2019

Abstract

To study naturalistic in-cabin emotion we developed SKYNIVI, a modified open source driving simulator, with scenarios designed to elicit startle and frustration. We target generating these emotions because we believe that by detecting these it will be possible for autonomous vehicles to learn to drive better. We show how to use SKYNIVI to develop datasets that capture naturalistic emotions in drivers and passengers for algorithmic development. We recruited 51 participants as dyads and single drivers to participate in two different scenarios. We show that we were able to evoke hundreds of instances of our target emotions in this cohort and present an analysis of factors we found to impact emotional expression including: scenario design , demographic factors, personality and baseline affect . We find that having a second person in the vehicle impacts observed expressions of emotion even when no difference in baseline affect is reported.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

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@INPROCEEDINGS{8813831, author={Alvarez, Ignacio and Healey, Jennifer and Lewis, Erica}, booktitle={2019 IEEE Intelligent Vehicles Symposium (IV)}, title={The SKYNIVI Experience: Evoking Startle and Frustration in Dyads and Single Drivers}, year={2019}, volume={}, number={}, pages={76-81}, keywords={Automobiles;Autonomous vehicles;Monitoring;Roads;Training}, doi={10.1109/IVS.2019.8813831} }

Alvarez, Ignacio and Healey, Jennifer and Lewis, Erica (2019). The SKYNIVI experience: evoking startle and frustration in dyads and single drivers. 2019 IEEE Intelligent Vehicles Symposium (IV).

Towards standardization of AV safety: C++ library for responsibility sensitive safety

Published in ‘Proceedings of the IEEE Intelligent Vehicle Symposium 2019’, IEEE 2019

Abstract

The need for safety in Automated Driving (AD) is becoming increasingly critical with the accelerating deployment of this technology. Beyond functional safety, industry must guarantee the operational safety of automated vehicles. Towards that end, Mobileye introduced the Responsibility Sensitive Safety (RSS), a model-based approach to Safety [1]. In this paper we expand upon this work introducing the C++ Library for Responsibility Sensitive Safety, an open source executable that implements a subset of RSS. We provide architectural details to integrate the C++ Library for Responsibility Sensitive Safety with AD Software pipelines as safety module overseeing decision making of driving policies. We illustrate this application with an example integration with the Baidu Apollo AD stack and simulator, [2] and [3], that provides safety validation of the planning module. Furthermore, we show how the C++ Library for Responsibility Sensitive Safety can be used to explore the usefulness of the RSS model through parameter exploration and analysis on minimum safe longitudinal distance, (dmin), considering different weather conditions. We also compare these results with half-of-speed rule followed in some parts of the world. We expect that the C++ Library for Responsibility Sensitive Safety becomes a critical component of future tools for formal verification, testing and validation of AD safety and that it helps bootstrap the AD research efforts towards standardization of safety.

Key Contributions

Safety analysis and validation
Standardized C++ library implementation
Modular software architecture design
Verification and validation methodology
Open-source safety model deployment

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@inproceedings{gassmann2019towards, title={Towards standardization of AV safety: C++ library for responsibility sensitive safety}, author={Gassmann, Bernd and Oboril, Fabian and Buerkle, Cornelius and Liu, Shuang and Yan, Shoumeng and Elli, Maria Soledad and Alvarez, Ignacio and Aerrabotu, Naveen and Jaber, Suhel and Van Beek, Peter and others}, booktitle={2019 IEEE Intelligent Vehicles Symposium (IV)}, pages={2265--2271}, year={2019}, organization={IEEE} }

Gassmann, Bernd and Oboril, Fabian and Buerkle, Cornelius and Liu, Shuang and Yan, Shoumeng and Elli, Maria Soledad and Alvarez, Ignacio and Aerrabotu, Naveen and Jaber, Suhel and Van Beek, Peter and others (2019). Towards standardization of AV safety: C++ library for responsibility sensitive safety. 2019 IEEE Intelligent Vehicles Symposium (IV).

Agents, environments, scenarios: A framework for examining models and simulations of human-vehicle interaction

Published in ‘Transportation research interdisciplinary perspectives’, 2020

Abstract

This paper provides a framework for examining human-vehicle interactions with respect to three dimensions that can involve models or simulations: the agents, the environments, and the scenarios. Agents are considered on a spectrum from human to artificial actors. Environments are considered on a spectrum from simulated to real. Scenarios are considered on a spectrum from constrained to unconstrained. It is argued that these three dimensions capture key differences in research approaches within the field of human-vehicle interaction, and that explicitly situating research and discussions within this framework will allow researchers to better compare and contrast research outcomes and contributions. The framework is used to locate different disciplines in the community with respect to one another, and to identify areas which are as-yet unexplored.

Key Contributions

Human-computer interaction design
Dimensions of simulation models
Automated Driving Development

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@article{janssen2020agents, title={Agents, environments, scenarios: A framework for examining models and simulations of human-vehicle interaction}, author={Janssen, Christian P and Boyle, Linda Ng and Ju, Wendy and Riener, Andreas and Alvarez, Ignacio}, journal={Transportation research interdisciplinary perspectives}, volume={8}, pages={100214}, year={2020}, publisher={Elsevier} }

Janssen, Christian P and Boyle, Linda Ng and Ju, Wendy and Riener, Andreas and Alvarez, Ignacio (2020). Agents, environments, scenarios: A framework for examining models and simulations of human-vehicle interaction. Transportation research interdisciplinary perspectives.

Auto-UI Global Perspectives

Published in ACM IX Interaction Journal, 2020

Abstract

ACM SIGCHI Auto-UI is a growing community, but one in which some continents were less involved than expected and hoped for. For the 2019 conference in Utrecht, the Netherlands, we made various targeted attempts to grow and diversify our international community, with support from the ACM SIGCHI Development Fund. Our efforts resulted in a growth in the number of Asian participants, which made up almost 20 percent of the attendees. In this blog, we briefly reflect on our initiatives and on a panel discussion focusing on research topics that matter more globally to the Auto-UI field.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

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Citation

@article{janssen2020auto, title={Auto-UI: global perspectives}, author={Janssen, Christian P and Schroeter, Ronald and Bidwell, Nicola J and Ji, Yong Gu and Alvarez, Ignacio and Bao, Shan and Jeon, Myounghoon and Boyle, Linda Ng and Donker, Stella F and Chuang, Lewis L and others}, journal={Interactions}, volume={27}, number={6}, pages={7--9}, year={2020}, publisher={ACM New York, NY, USA} }

Janssen, Christian P and Schroeter, Ronald and Bidwell, Nicola J and Ji, Yong Gu and Alvarez, Ignacio and Bao, Shan and Jeon, Myounghoon and Boyle, Linda Ng and Donker, Stella F and Chuang, Lewis L and others (2020). Auto-UI Global Perspectives. Interactions.

Emotion GaRage Vol. II A Workshop on Affective In-Vehicle Display Design

Published in ‘Proceedings of 12th International Conference in Automotive User Interfaces and Interactive Vehicular Applications’, 2020

Abstract

Driver performance and behavior can be partially predicated based on one’s emotional state. Through ascertaining the emotional state of passengers and employing various mitigation strategies, empathic cars can show potential in improving user experience and driving performance. Challenges remain in the implementation of such strategies, as individual differences play a large role in mediating the effect of affective intervention. Therefore, we propose a workshop that aims to bring together researchers and practitioners interested in affective interfaces and in-vehicle technologies as a forum for the development of targeted emotion intervention methods. During the workshop, we will focus on a common set of use cases and generate approaches that can suit different user groups. By the end of this short workshop, researchers will determine ideal intervention methods for prospective user groups. This will be achieved through the method of insight combination to generate and discuss ideas.

Key Contributions

Affective Computing in Automotive
Emotion-Aware HMI Design
Community Building

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Citation

@misc{nadri2020emotion, title={Emotion GaRage Vol. II}, author={Nadri, Chihab and Bosch, E and Oehl, M and Alvarez, I and Braun, M and Jeon, M}, year={2020}, publisher={II} }

Nadri, Chihab and Bosch, E and Oehl, M and Alvarez, I and Braun, M and Jeon, M (2020). Emotion GaRage Vol. II A Workshop on Affective In-Vehicle Display Design. .

Evaluation of responsibility-sensitive safety (rss) model based on human-in-the-loop driving simulation

Published in In proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, IEEE 2020.

Abstract

Safety is an important challenge in the development of automated vehicles (AVs). To help with the challenge of achieving higher safety in the decision making of AVs, Intel and Mobileye have proposed a parameterized model named Responsibility-Sensitive Safety (RSS). Previous studies have demonstrated that RSS has the potential to improve the safety performance of automated vehicles. However, RSS could lead to a considerable car-following distance depending on the parameter values chosen for the model, which could reduce traffic efficiency. To improve the efficiency of RSS applied to Adaptive Cruise Control (ACC) systems, previous work proposed an efficiency-optimal (referred as “Efficiency-optimal RSS”) variation of the RSS model that involves different triggering conditions of a proper response. Therefore, in this paper a human-in-the-loop driving simulation experiment was conducted to evaluate the performance and acceptance of different safety methods. The RSS model and the efficiencyoptimal variant were embedded in an ACC system based on Model Predictive Control (MPC) algorithm. Two car-following scenarios with a sudden deceleration of lead vehicle at various time headways were simulated to evaluate the human perception and response of the different models. Results show that the original RSS model improves subjective safety judgment of human drivers. While the Efficiency-optimal RSS variant has a lower subjective safety score when compared to original RSS, it significantly reduces driver’s emergency braking reactions when compare to an ACC only system.

Key Contributions

Human-in-the-loop evaluation framework
Comparative performance analysis (RSS vs. Human)
Safety model parameter sensitivity analysis
Empirical validation of a formal safety model
Simulation-based model calibration methodology

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@inproceedings{chai2020evaluation, title={Evaluation of responsibility-sensitive safety (rss) model based on human-in-the-loop driving simulation}, author={Chai, Chen and Zeng, Xianming and Alvarez, Ignacio and Elli, Maria Soledad}, booktitle={2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)}, pages={1--7}, year={2020}, organization={IEEE} }

Alvarez, I. (2020). . 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC).

How safe is safe enough? Automatic safety constraints boundary estimation for decision-making in automated vehicles

Published in Proceedings of IEEE Intelligent Vehicle Symposium 2020, IEEE 2020.

Abstract

The determination of safety assurances for automated driving vehicles is one of the most critical challenges in the industry today. Several behavioral safety models for automated driving have been proposed recently and standards discussions are on the way. In this paper we present a method to automatically explore the performance of automated vehicle (AV) safety models utilizing robustness of Metric Temporal Logic (MTL) specifications as a continuous metric of safety. We present a case study of the Responsibility Sensitive Safety model (RSS), introducing a safety evaluation pipeline based on the CARLA driving simulator, RSS and a set of safety-critical driving scenarios. Our method automatically extracts safety relevant profiles for these scenarios providing practical parametric boundaries for implementation. Furthermore, we evaluate the trade-offs between safety and utility within the safe RSS parameter space through a proposed naturalistic benchmark challenge that we open-sourced. We analyze different RSS parameter configurations including assertive and more conservative settings, extracted by our specification-driven framework. Our results show that while maintaining the safety boundaries, the extracted RSS configuration for assertive driving behavior achieves the highest utility.

Key Contributions

Safety boundary estimation framework
Parametric and non-parametric estimation methods
Simulation-based data generation and validation
Real-world data application and analysis
Systematic safety parameter definition

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@inproceedings{rodionova2020safe, title={How safe is safe enough? Automatic safety constraints boundary estimation for decision-making in automated vehicles}, author={Rodionova, Al{"e}na and Alvarez, Ignacio and Elli, Maria Soledad and Oboril, Fabian and Quast, Johannes and Mangharam, Rahul}, booktitle={2020 IEEE Intelligent Vehicles Symposium (IV)}, pages={1457--1464}, year={2020}, organization={IEEE} }

Alvarez, I. (2020). . 2020 IEEE Intelligent Vehicles Symposium (IV).

PURSS: Towards Perceptual Uncertainty Aware Responsibility Sensitive Safety with ML.

Published in ‘Proceedings of 2020 AAAI Conference’, AAAI 2020

Abstract

Automated driving is an ML-intensive problem and its safety depends on the integrity of perception as well as planning and control. Responsibility Sensitive Safety (RSS) is a recent approach to promote safe planning and control that relies on perfect perception; however, perceptual uncertainty is always present, and this causes the possibility of misperceptions that can lead an autonomous vehicle to allow unsafe actions. In this position paper, we sketch a novel proposal for a formal model of perception coupled with RSS to help mitigate the impact of misperception by using information about perceptual uncertainty. The approach expresses uncertainty as imprecise perceptions that are consumed by RSS and cause it to limit actions to those that support safe behaviour given the perceptual uncertainty. We illustrate our approach using examples and discuss its implications and limitations.

Key Contributions

Safety analysis and validation

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Citation

@inproceedings{salay2020purss, title={PURSS: Towards Perceptual Uncertainty Aware Responsibility Sensitive Safety with ML.}, author={Salay, Rick and Czarnecki, Krzysztof and Elli, Maria Soledad and Alvarez, Ignacio J and Sedwards, Sean and Weast, Jack}, booktitle={SafeAI@ AAAI}, pages={91--95}, year={2020}, organization={New York} }

Salay, Rick and Czarnecki, Krzysztof and Elli, Maria Soledad and Alvarez, Ignacio J and Sedwards, Sean and Weast, Jack (2020). PURSS: Towards Perceptual Uncertainty Aware Responsibility Sensitive Safety with ML.. SafeAI@ AAAI.

MISO-V: Misbehavior detection for collective perception services in vehicular communications

Published in ‘Proceedings of IEEE Intelligent Transportation Systems Conference 2021’, IEEE 2021

Abstract

Recently, Collective Perception Messages (CPM) that carry additional information about the surrounding environment beyond Basic Safety Messages (BSM) or Cooperative Awareness Messages (CAM) have been proposed to increase the situational awareness for Connected and Automated Vehicles (CAV) in Intelligent Transportation Systems. However, blindly trusting perception information from neighbors that cannot be locally verified is dangerous given the safety impact that erroneous or malicious information might have. This paper addresses the data trust challenge of CPMs, proposing a misbehavior detection scheme called MISO- V (Multiple Independent Sources of Observations over V2X) that leverages the inherently overlapping nature of the perception observations from multiple vehicles to verify the semantic correctness of the V2X data and improve the data trust and robustness of V2X systems. CPM-enabled CAVs are implemented and MISO-V performance is evaluated in CARLA-based simulation tool, where falsified V2X packets presenting a ghost car are injected in a suburban T-junction scenario with other cars. The results show that MISO- V is very effective in detecting the ghost car attacks and removing the impact of such misbehavior from influencing the receiver and offers a conservative and sensible approach towards trustworthy Collective Perception Services for CAVs.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

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Citation

@INPROCEEDINGS{9575970, author={Liu, Xiruo and Yang, Lily and Alvarez, Ignacio and Sivanesan, Kathiravetpillai and Merwaday, Arvind and Oboril, Fabian and Buerkle, Cornelius and Sastry, Manoj and Baltar, Leonardo Gomes}, booktitle={2021 IEEE Intelligent Vehicles Symposium (IV)}, title={MISO- V: Misbehavior Detection for Collective Perception Services in Vehicular Communications}, year={2021}, volume={}, number={}, pages={369-376}, keywords={Simulation;Semantics;Redundancy;Receivers;Tools;Robustness;Safety}, doi={10.1109/IV48863.2021.9575970} }

Liu, Xiruo and Yang, Lily and Alvarez, Ignacio and Sivanesan, Kathiravetpillai and Merwaday, Arvind and Oboril, Fabian and Buerkle, Cornelius and Sastry, Manoj and Baltar, Leonardo Gomes (2021). MISO-V: Misbehavior detection for collective perception services in vehicular communications. 2021 IEEE Intelligent Vehicles Symposium (IV).

On responsibility sensitive safety in car-following situations-a parameter analysis on german highways

Published in Proceedings of the IEEE Intelligent Vehicles Symposium (IV), IEEE 2021

Abstract

The need for safety in automated driving is undisputed. Since automated vehicles are expected to reduce the number of fatalities in road traffic significantly, hundreds of millions of test kilometers would be required for statistical safety validation [1]. Physics-based safety verification approaches are promising in order to reduce this validation effort. Towards this goal, Mobileye introduced the concept of Responsibility-Sensitive Safety (RSS). In RSS, bounds for the reasonable worst-case behavior of traffic participants are assumed to be given, such as the reaction time or the maximum deceleration. These parameters have a crucial effect on the applicability of the approach: choosing conservative parameters likely hinders traffic flow, while the opposite could lead to collisions, as the assumptions are violated. Thus, in this work, we focus on finding reasonable parameters of RSS. Based on the physical limits, legal requirements and human driving behavior, we propose scopes and parameter sets that allow for a sound safety verification while not hindering traffic flow. Furthermore, we present an approach that explains seemingly frequent human drivers’ RSS violations on highways and may lead to a useful extension of RSS.

Key Contributions

Safety analysis and validation
Automated driving system development
Verification and validation methods
Parameter Analysis

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Citation

@INPROCEEDINGS{9575420, author={Naumann, Maximilian and Wirth, Florian and Oboril, Fabian and Scholl, Kay–Ulrich and Elli, Maria Soledad and Alvarez, Ignacio and Weast, Jack and Stiller, Christoph}, booktitle={2021 IEEE Intelligent Vehicles Symposium (IV)}, title={On Responsibility Sensitive Safety in Car-following Situations - A Parameter Analysis on German Highways}, year={2021}, volume={}, number={}, pages={83-90}, keywords={Law;Intelligent vehicles;Roads;Safety;Brakes;Responsibility-Sensitive Safety (RSS);Automated Driving (AD);Safety Verification;Parameter Analysis}, doi={10.1109/IV48863.2021.9575420}}

Alvarez, I. (2021). . 2021 IEEE Intelligent Vehicles Symposium (IV).

To err is human: The role of human derived safety metrics in an age of automated vehicles

Published in ‘SAE Technical Paper’, SAE 2021

Abstract

As industry races to complete technical development of automated driving systems (ADS), important questions are being raised about how to measure the safety of such systems and the overall safety of Automated Vehicles (AVs). Traffic safety engineers have for decades utilized metrics to assess the safety of human drivers and measurements such as Time To Collision (TTC) and Time Headway (THW) have proven to be a useful indicator of increased risk of an accident for human drivers. But what if we can do better with AVs? Are human driving derived risk metrics meaningful for a self-driving vehicle? Recently, the Institute for Automated Mobility (IAM) published a set of metrics defined specifically for self-driving vehicles that provide a thorough assessment of the safety of an AV. While humans must use estimation and cautious judgement to make decisions, AVs can use precise measurement techniques via sensors and correlate multiple sources of data in real time. Utilizing information such as the reaction time of the ADS, the braking capability of the AV and more, the IAM proposed metrics allow for the assessment of the safety of an AV to be accurately measured, not as a notion of approximated risk, but as a binary calculation of safety. In this paper we analyze, compare and contrast human driving, risk-oriented safety metrics with the more definitive metrics proposed for AVs. We answer important questions about the necessary evolution of human derived metrics to ensure they are meaningful in the assessment of the safety of an AV, as well as whether novel metrics proposed for AVs can be used to better understand and assess the safety performance of AVs when compared to historical safety measures. Our research proves that AV-based assessment metrics can provide better insight into the safety of both AVs and human drivers.

Key Contributions

Safety analysis and validation
Automated driving system development

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Citation

@techreport{weast2021err, title={To err is human: The role of human derived safety metrics in an age of automated vehicles}, author={Weast, Jack and Elli, Maria and Alvarez, Ignacio}, year={2021}, institution={SAE Technical Paper} }

Weast, Jack and Elli, Maria and Alvarez, Ignacio (2021). To err is human: The role of human derived safety metrics in an age of automated vehicles. .

Driver emotions in automated vehicles

Abstract

There is little doubt that driving generates emotional responses, whether that’s the joy of freedom, the boredom of stop-and-go traffic or anger towards unsafe maneuvers. In this chapter we provide an overview of emotion research applied to the automotive context and highlight the impact of emotional states in varying levels of driving automation. We review the most critical research findings on the impact of emotional states in driving performance including reaction time and take-over readiness. We also discuss the application of emotion regulation strategies related to the driving task. Finally, we analyze the research challenges still present for robust emotional classification and personalization in their application to in-vehicle interactions. This technology offers great potential for the development of emotionally-aware in-cabin driver assistants which will play a critical role in the future of automated driving user experience development.

Key Contributions

Foundational theory compilation
UX design methods and frameworks
Interface application and case studies
Academia-industry knowledge transfer
Holistic user-centered design advocacy

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Citation

@incollection{fakhrhosseini2022driver, title={Driver emotions in automated vehicles}, author={FakhrHosseini, Shabnam and Ko, Sangjin and Alvarez, Ignacio and Jeon, Myounghoon}, booktitle={User Experience Design in the Era of Automated Driving}, pages={85--97}, year={2022}, publisher={Springer} }

Alvarez, I. (2022). . User Experience Design in the Era of Automated Driving.

Emotion GaRage Vol. III: A Workshop on Affective In-Vehicle Display Applications

Published in ‘Adjunct Proceedings of the 14th International Conference on Automotive User Interfaces and Interactive Vehicular Applications’, ACM 2022

Abstract

Empathic in-vehicle interfaces can address driver affect and mitigate decreases in driving performance and behavior that are associated with emotional states. Empathic vehicles can detect and employ a variety of intervention modalities to change user affect and improve user experience. Challenges remain in the implementation of such strategies, as a broader established view of practical intervention modalities and strategies is still absent. Therefore, we propose a workshop that aims to bring together researchers and practitioners interested in affective interfaces and in-vehicle technologies as a forum for the development of displays and alternatives suitable to various use case situations in current and future vehicle states. During the workshop, we will focus on a common set of use cases and generate approaches that can suit different user groups. By the end of this workshop, researchers will create a design flowchart for in-vehicle affective display designers when creating displays for an empathic vehicle.

Key Contributions

HCI Interaction Design
Emotional and affective computing
HCI Prototyping
Workshop organization and community building

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Citation

@inproceedings{10.1145/3544999.3550161, author = {Nadri, Chihab and Dong, Jiayuan and Li, Jingyi and Alvarez, Ignacio and Jeon, Myounghoon}, title = {Emotion GaRage Vol. III: A Workshop on Affective In-Vehicle Display Applications}, year = {2022}, isbn = {9781450394284}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3544999.3550161}, doi = {10.1145/3544999.3550161}, pages = {186–188}, numpages = {3}, keywords = {Empathic vehicles, affective computing, emotions, human-centered computing, interaction design, user experience}, location = {Seoul, Republic of Korea}, series = {AutomotiveUI '22} }

Nadri, Chihab and Dong, Jiayuan and Li, Jingyi and Alvarez, Ignacio and Jeon, Myounghoon (2022). Emotion GaRage Vol. III: A Workshop on Affective In-Vehicle Display Applications. .

Empathic vehicle design: Use cases and design directions from two workshops

Published in ‘Proceedings of ACM’s Computer Human Interaction Conference (CHI)’, ACM 2022

Abstract

Empathic vehicles are expected to improve user experience in automated vehicles and to help increase user acceptance of technology. However, little is known about potential real-world implementations and designs using empathic interfaces in vehicles with higher levels of automation. Given advances in affect detection and emotion mitigation, we conducted two workshops (N1 =24, N2 = 22, Ntotal = 46) on the design of empathic vehicles and their potential utility in a variety of applications. This paper recapitulates key opportunities in the design and application of empathetic interfaces in automated vehicles which emerged from the two workshops hosted at the ACM AutoUI conferences.

Key Contributions

Emphathetic HMI design in Automotive
Generative AI prototyping
Workshop organization and community building

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Citation

@inproceedings{nadri2022empathic, title={Empathic vehicle design: Use cases and design directions from two workshops}, author={Nadri, Chihab and Alvarez, Ignacio and Bosch, Esther and Oehl, Michael and Braun, Michael and Healey, Jennifer and Jallais, Christophe and Ju, Wendy and Li, Jingyi and Jeon, Myounghoon}, booktitle={CHI Conference on Human Factors in Computing Systems Extended Abstracts}, pages={1--7}, year={2022} }

Nadri, Chihab and Alvarez, Ignacio and Bosch, Esther and Oehl, Michael and Braun, Michael and Healey, Jennifer and Jallais, Christophe and Ju, Wendy and Li, Jingyi and Jeon, Myounghoon (2022). Empathic vehicle design: Use cases and design directions from two workshops. CHI Conference on Human Factors in Computing Systems Extended Abstracts.

IEEE Standard for Assumptions in Safety-Related Models for Automated Driving Systems

Abstract

This standard applies to road vehicles. It defines a minimum set of reasonable assumptions and foreseeable scenarios that shall be considered in the development of safety related models that are part of an automated driving system (ADS). Scope: This standard applies to road vehicles. For a set of scenarios, a minimum set of assumptions regarding reasonably foreseeable behaviors of other road users are defined that shall be considered in the development of safety-related models for automated driving systems (ADS). This standard further defines a list of attributes common to contributed safety-related models and methods to help verify whether a safety-related model takes the minimum set of assumptions into consideration. An informative annex instantiates several examples of how the proposed minimum set of assumptions could be employed in ADS development. Sources of uncertainty, such as prediction or perception errors, are out of scope to this standard. This standard does not guarantee the safety of the overall system in all scenarios. Purpose: Government and Industry alike need an open, transparent, and technology-neutral standard that provides guidance useful for evaluating the performance of an ADS. This guidance consists of a minimum set of assumptions with bounds on reasonably foreseeable behaviors of other road users used in the development of safety-related models.

Key Contributions

Minimum assumption definition for ADAS
Reasonable Foreseeable scenario specification
Technology-neutral evaluation guidance
Model verification methodology

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BibTeX

@ARTICLE{9761121, author={}, journal={IEEE Std 2846-2022}, title={IEEE Standard for Assumptions in Safety-Related Models for Automated Driving Systems}, year={2022}, volume={}, number={}, pages={1-59}, keywords={IEEE Standards;Autonomous vehicles;Vehicle safety;Autonomous driving;Vehicle driving;Data privacy;Decision making;ADS;ADS-operated vehicle;automated driving system;assumption;automated vehicle;autonomous vehicles;AV;decision-making;IEEE 2846}, doi={10.1109/IEEESTD.2022.9761121}}

Quantifying Error Propagation in Multi-Stage Perception System of Autonomous Vehicles via Physics-Based Simulation

Published in Proceedings of the IEEE 2022 Winter Simulation Conference, IEEE 2023

Abstract

Ensuring the safety of autonomous vehicle (AV) relies on accurate prediction of error occurrences in its perception system. Due to the inter-stage functional dependence, the error occurred at a certain stage may be propagated to the following stage and generate extra errors. To quantify the error propagation, this paper adopts the physics-based simulation, which enables fault injection at different stages of an AV perception system to generate error event data for error propagation modeling. A multi -stage Hawkes process (MSHP) is proposed to predict the error occurrences in each stage, with error propagation represented as a latent triggering mechanism. With explicitly considering the error propagation mechanism, the proposed outperforms benchmark methods in predicting error occurrence in a physics-based simulation of a multistage AV perception system. The proposed two-step likelihood-based algorithm accurately estimates the model coefficients in a numerical simulation case study.

Key Contributions

Integrated simulation and modeling framework
Fault-injection simulation for data generation
Statistical error propagation modeling
Model parameter estimation algorithm
Model validation and performance benchmarking

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Citation

@inproceedings{pan2022quantifying, title={Quantifying error propagation in multi-stage perception system of autonomous vehicles via physics-based simulation}, author={Pan, Fenglian and Zhang, Yinwei and Head, Larry and Liu, Jian and Elli, Maria and Alvarez, Ignacio}, booktitle={2022 Winter Simulation Conference (WSC)}, pages={2511--2522}, year={2022}, organization={IEEE} }

Alvarez, I. (2022). . 2022 Winter Simulation Conference (WSC).

Special Issue

Published in ‘MDPI Sensors’, 2022

Abstract

Connected and automated driving technologies have the potential to revolutionize transportation by facilitating mobility services to a wider population, improving safety and traffic efficiency. Automated driving technology is expected to reduce the number of accidents caused by human error and avert deadly crashes, ensure mobility for all, including old and impaired individuals, allow the human driver to perform alternative (secondary) tasks, increase traffic flow efficiency, reduce fuel consumption, and lower emissions.

Driven by these goals, humankind is experiencing an exponential growth in vehicle automation taking over the monitoring of surroundings and vehicle control tasks from human drivers in a quest towards full autonomy. Connected and automated vehicles are equipped with multimodal sensors that allow continuous perception and monitoring of driving tasks to assist drivers in lower levels of SAE automation or to fully take control of driving tasks under full SAE automation. Numerous sensors, both inside and outside vehicles, allow the detection and identification of oncoming obstacles, the determination of their velocity, and the prediction of future behaviours to avoid potential collisions. Each sensor has its own strengths and weaknesses in terms of range, accuracy, energy consumption. and sensitivity towards external conditions such as weather and light. Automated vehicles usually rely on a mix of signals to improve operational reliability and robustness under the dynamic external conditions of real-world deployments. Generally, we can divide external AV sensors into two major groups: active and passive. Active sensors generate an active signal (electromagnetic or light) transmitted to the external environment to analyse its reflection (e.g., radar, lidar), whereas passive sensors just record the information from the environment (e.g., camera). Additionally, there have been advances in intelligent transportation infrastructure to monitor road users, perform predictive analytics, and facilitate collaborative perception services and remote vehicle control.

The increasing commercial availability of conditional automation (SAE level 3) and the incoming Robotaxi services (SAE Level 4) have also resulted in an increase in in-cabin monitoring sensors dedicated to monitoring driver and passenger behaviours. Multimodal in-cabin monitoring systems are crucial enablers for successfully managing automated vehicle operations. These systems enable the detection of the driver/passenger’s physiological state and activity to assess their readiness to take over control of the vehicle if required as well as to monitor their safety. Driving monitoring solutions provide information on occupants’ fatigue, distraction, discomfort, and stress. Furthermore, they can help to verify that automation is used properly by evaluating engagement in the driving monitoring task or the inherent risk of the non-driving tasks.

This Special issue aims to collect original theoretical or empirical articles on different sensing technologies, solutions, and applications for automated vehicles.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

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Citation

@article{SpecialIssue, author = {Ignacio Alvarez, Jaka Sodnik and Nikolas Thomopoulos}, title = {Robust Multimodal Sensing for Automated Driving Systems}, journal = {Sensors}, year = {2022}, volume = {22}, issue = {13}, pages = {}, doi = {ISSN 1424-8220} }

Ignacio Alvarez, Jaka Sodnik and Nikolas Thomopoulos (2022). Special Issue. Sensors.

User experience design in the era of automated driving

Published in Journal Name, 2023

Abstract

This book is dedicated to user experience design for automated driving to address humane aspects of automated driving, e.g., workload, safety, trust, ethics, and acceptance. Automated driving has experienced a major development boost in recent years. However, most of the research and implementation has been technology-driven, rather than human-centered. The levels of automated driving have been poorly defined and inconsistently used. A variety of application scenarios and restrictions has been ambiguous. Also, it deals with human factors, design practices and methods, as well as applications, such as multimodal infotainment, virtual reality, augmented reality, and interactions in and outside users. This book aims at 1) providing engineers, designers, and practitioners with a broad overview of the state-of-the-art user experience research in automated driving to speed-up the implementation of automated vehicles and 2) helping researchers and students benefit from various perspectives and approaches to generate new research ideas and conduct more integrated research.

Key Contributions

State-of-the-art UX Design in Automated Driving
Foundational theory compilation
UX design methods and frameworks
Interface application and case studies

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Citation

@book{riener2022user, title={User experience design in the era of automated driving}, author={Riener, Andreas and Jeon, Myounghoon and Alvarez, Ignacio}, volume={980}, year={2022}, publisher={Springer} }

Alvarez, I. (2022). . .

White Paper-Literature Review on Kinematic Properties of Road Users for Use on Safety-Related Models for Automated Driving Systems

Published in IEEE Standards, 2022

Abstract

This document presents a review of relevant literature (e.g., standards, regulations, and scientific publications) that investigated kinematic behavior of road users. This review is intended to serve as a key contribution to the Automated Driving Systems (ADS) research and industry communities, as well as to current standardization efforts, such as IEEE Std 2846, IEEE Standard for Assumptions in Safety-Related Models for Automated Driving Systems.

Key Contributions

Literature review road actors kinematic behavior
Systematic data organization of kinematic parameter values
Kinematic data contextualization
Research gap analysis

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Citation

@ARTICLE{9763462, author={IEEE VT, ITS}, journal={Literature Review on Kinematic Properties of Road Users for Use on Safety-Related Models for Automated Driving Systems}, title={Literature Review on Kinematic Properties of Road Users for Use on Safety-Related Models for Automated Driving Systems}, year={2022}, volume={}, number={}, pages={1-35}, keywords={ADS;automated driving systems;behaviors;IEEE 2846;kinematic;literature review;road users;standards;white paper}, doi={} }

Alvarez, I. (2022). . Literature Review on Kinematic Properties of Road Users for Use on Safety-Related Models for Automated Driving Systems.

Application of Responsibility-Sensitive Safety in Areas with Limited Visibility: Occlusions in RSS

Published in ‘2023 IEEE 26th International Conference on Intelligent Transportation Systems’, IEEE 2023

Abstract

Areas of limited visibility are common in day-to-day traffic: be it static buildings, parked vehicles, traffic participants blocking the vehicle’s line of sight, harsh weather conditions or just narrow curves that impede the automated driving sensor suite to inspect the road ahead. Autonomous vehicles have to be able to safely cope with this kind of constraints. The Responsibility-Sensitive Safety model (RSS) demands vehicles to exercise caution with respect to occlusions and to consider also occluded road agents. This paper provides a concrete implementation of how occlusions in RSS can be addressed and investigates the balance between safety and usefulness of the model when a reasonably foreseeable behavior of occluded road agents is assumed. We perform occlusion experiments in urban as well as on highway scenarios with the driving simulation platform CARLA applying different parameterization of the agents kinematic properties and the safety model parameters to analyse and judge the consequences with respect to safe driving and overcautious driving behaviors.

Key Contributions

Safety analysis and validation
Implements occlusion handling with artificial vehicles
Evaluates approach in urban/highway scenarios
Shows aggressive parameters can reduce usability
Analyzes real-world data for assumptions
Demonstrates collision mitigation via simulation

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Citation

@INPROCEEDINGS{10421912, author={Gassmann, Bernd and Dey, Shreya and Alvarez, Ignacio and Oboril, Fabian and Scholl, Kay-Ulrich}, booktitle={2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)}, title={Application of Responsibility-Sensitive Safety in Areas with Limited Visibility: Occlusions in RSS}, year={2023}, volume={}, number={}, pages={5976-5981}, keywords={Roads;Kinematics;Safety;Behavioral sciences;Usability;Intelligent transportation systems;Meteorology}, doi={10.1109/ITSC57777.2023.10421912} }

Gassmann, Bernd and Dey, Shreya and Alvarez, Ignacio and Oboril, Fabian and Scholl, Kay-Ulrich (2023). Application of Responsibility-Sensitive Safety in Areas with Limited Visibility: Occlusions in RSS. 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC).

Emotion GaRage Vol. IV: Creating empathic in-vehicle interfaces with generative AIs for automated vehicle contexts

Published in ‘Proceedings of ACM’s Automotive UI 2023’, ACM 2023

Abstract

This workshop aims to design advanced empathic user interfaces for in-vehicle displays, particularly for high-level automated vehicles (SAE level 3 or higher). Incorporating model-based approaches for understanding human emotion regulation, it seeks to enhance the user-vehicle interaction. A unique aspect of this workshop is the integration of generative artificial intelligence (AI) tools in the design process. The workshop will explore generative AI’s potential in crafting contextual responses and its impact on user experience and interface design. The agenda includes brainstorming on various driving scenarios, developing emotion-oriented intervention methods, and rapid prototyping with AI tools. The anticipated outcome includes practical prototypes of affective user interfaces and insights on the role of AI in designing human-machine interactions. Through this workshop, we hope to contribute to making automated driving more accessible and enjoyable.

Key Contributions

Automated driving system development
Emotional and affective computing
Designs advanced empathic user interfaces
Integrates generative AI in design process
Applies model-based emotion regulation methods

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Citation

@inproceedings{10.1145/3581961.3609828, author = {Choe, Mungyeong and Bosch, Esther and Dong, Jiayuan and Alvarez, Ignacio and Oehl, Michael and Jallais, Christophe and Alsaid, Areen and Nadri, Chihab and Jeon, Myounghoon}, title = {Emotion GaRage Vol. IV: Creating Empathic In-Vehicle Interfaces with Generative AIs for Automated Vehicle Contexts}, year = {2023}, isbn = {9798400701122}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3581961.3609828}, doi = {10.1145/3581961.3609828}, booktitle = {Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages = {234–236}, numpages = {3}, keywords = {ChatGPT, affective computing, emotions, empathic vehicles, interaction design}, location = {Ingolstadt, Germany}, series = {AutomotiveUI '23 Adjunct} }

Choe, Mungyeong and Bosch, Esther and Dong, Jiayuan and Alvarez, Ignacio and Oehl, Michael and Jallais, Christophe and Alsaid, Areen and Nadri, Chihab and Jeon, Myounghoon (2023). Emotion GaRage Vol. IV: Creating empathic in-vehicle interfaces with generative AIs for automated vehicle contexts. Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

“Play Your Anger”: A report on the empathic in-vehicle interface workshop

Published in ‘Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications’, 2023

Abstract

Empathic in-vehicle interfaces are critical in improving user safety and experiences. There has been much research on how to estimate drivers’ affective states, whereas little research has investigated intervention methods that mitigate potential impacts from the driver’s affective states on their driving performance and user experiences. To enhance the development of in-vehicle interfaces considering emotional aspects, we have organized a workshop series to gather automotive user interface experts to discuss this topic at the International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI). The present paper focuses particularly on the intervention methods created by the experts and proposes design recommendations for future empathic in-vehicle interfaces. We hope this work can spark lively discussions on the importance of drivers’ affective states in their user experience of automated vehicles and pose the right direction.

Key Contributions

Presents novel empathic interface prototypes.
Offers expert-driven design recommendations.
Addresses anger, frustration, and fear.
Details a three-phase design process.
Focuses on specific intervention methods.

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Citation

@inproceedings{10.1145/3581961.3609865, author = {Dong, Jiayuan and Nadri, Chihab and Alvarez, Ignacio and Diels, Cyriel and Lee, Myeongkyu and Li, Jingyi and Liao, Pei Hsuan and Manger, Carina and Sadeghian, Shadan and Schu\ss{}, Martina and Walker, Bruce N. and Walker, Francesco and Wang, Yiyuan and Jeon, Myounghoon}, title = {“Play Your Anger”: A Report on the Empathic In-vehicle Interface Workshop}, year = {2023}, isbn = {9798400701122}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3581961.3609865}, doi = {10.1145/3581961.3609865}, booktitle = {Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages = {168–173}, numpages = {6}, location = {Ingolstadt, Germany}, series = {AutomotiveUI '23 Adjunct} }

Dong, Jiayuan and Nadri, Chihab and Alvarez, Ignacio and Diels, Cyriel and Lee, Myeongkyu and Li, Jingyi and Liao, Pei Hsuan and Manger, Carina and Sadeghian, Shadan and Schu\ss{ (2023). “Play Your Anger”: A report on the empathic in-vehicle interface workshop. Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

RSS Demonstrator: a Tool for User Experience Interactions with Automated Driving Safety Models

Published in ‘Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications’, 2023

Abstract

Safety Assurance remains a challenge for the large-scale deployment of Automated Driving Systems (ADS). Safety models monitor the performance of the ADS. Most safety models are validated both in simulation and during on-road tests. However, first-hand experiences and analysis of ADS safety models are not easily accessible to the general research community. This paper introduces the RSS driving demonstrator an open-source simulation tool that enables first-hand experience of the Responsibility Sensitive Safety (RSS) safety model proposed by Intel and Mobileye and adopted by several Automotive Industry standards and regulatory frameworks. The RSS demonstrator enables first-hand interactions and experience of ADS safety model restrictions in both automated and manual driving conditions. As a User Experience (UX) tool, it provides quantitative safety metrics and flexible user interaction features. The results indicate it served to both evangelize the RSS ADS safety model with laymen population and is a versatile tool for Automotive UX development.

Key Contributions

Safety analysis and validation
User experience design and evaluation
Automated driving system development
Tool development and demonstration
Human-computer interaction design

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Citation

@inproceedings{10.1145/3581961.3609894, author = {Alvarez, Ignacio and Gassmann, Bernd and Pasch, Frederik and Oboril, Fabian}, title = {RSS Demonstrator: a Tool for User Experience Interactions with Automated Driving Safety Models}, year = {2023}, isbn = {9798400701122}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3581961.3609894}, doi = {10.1145/3581961.3609894}, pages = {180–185}, numpages = {6}, keywords = {Automated Driving Safety Model, Automated Driving System, Driving Simulation, RSS, Safety, User Experience}, location = {Ingolstadt, Germany}, series = {AutomotiveUI '23 Adjunct} }

Alvarez, Ignacio and Gassmann, Bernd and Pasch, Frederik and Oboril, Fabian (2023). RSS Demonstrator: a Tool for User Experience Interactions with Automated Driving Safety Models. .

Inside Out: Emotion GaRage Vol. V

Published in ‘Adjunct Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications’, 2024

Abstract

The rapid advancement of automated vehicles has aroused the curiosity of researchers in the automotive field. Understanding the emotional aspects of this technology is critical to improving human-vehicle interactions. The topics of the proposed workshop will be expanded from internal to external empathetic interface designs of automated vehicles. The workshop will gather researchers and practitioners to brainstorm and design affective internal and external interfaces for automated vehicles, targeting specific use cases within the social context. During the workshop, participants will use an affective design tool and generative AI to prototype affective interface designs in automated vehicles. With this creative approach, we aim to expand the knowledge of affective eHMIs in addition to in-vehicle designs and understand social factors that contribute to the user perceptions of automated vehicles.

Key Contributions

Emotional and affective computing
Expands focus to external vehicle interfaces
Aims to create design guidelines
Addresses social context and ethical issues
Prototype affective interface designs

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Citation

@inproceedings{10.1145/3641308.3677403, author = {Dong, Jiayuan and Gowda, Nikhil and Wang, Yiyuan and Choe, Mungyeong and Alsaid, Areen and Alvarez, Ignacio and Krome, Sven and Jeon, Myounghoon}, title = {Inside Out: Emotion GaRage Vol. V}, year = {2024}, isbn = {9798400705205}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3641308.3677403}, doi = {10.1145/3641308.3677403}, pages = {260–263}, numpages = {4}, keywords = {affective external human-machine interaction designs, emotions, empathic in-vehicle interfaces, generative artificial intelligence}, location = {Stanford, CA, USA}, series = {AutomotiveUI '24 Adjunct} }

Dong, Jiayuan and Gowda, Nikhil and Wang, Yiyuan and Choe, Mungyeong and Alsaid, Areen and Alvarez, Ignacio and Krome, Sven and Jeon, Myounghoon (2024). Inside Out: Emotion GaRage Vol. V. .

Reliability modeling for perception systems in autonomous vehicles: A recursive event-triggering point process approach

Published in ‘Transportation Research Part C: Emerging Technologies’, 2024

Abstract

Ensuring the reliability of sensor-fusion-based perception systems is crucial for the safe deployment of autonomous vehicles. Such systems function through a sequence of interconnected stages, where errors in upstream stages may propagate to downstream stages and trigger additional errors. The cross-stage error propagation conceptually exists and makes errors in different stages, not independent, posing model challenges, estimation challenges, and data challenges for reliability modeling. The existing methods cannot be applied to address all these challenges. Thus, this paper presents a recursive event-triggering point process to explicitly consider the error propagation based on the simulated data. The data are simulated from a proposed error injection framework, which can generate various errors from a sequence of interconnected stages in a perception system. The latent and probabilistic error propagation information is incorporated into a modified expectation–maximization (EM) algorithm for parameter estimation. The numerical and physics-based simulation case studies demonstrate the prediction accuracy and interpretability of the proposed modeling methodology.

Key Contributions

Automated driving system development
Proposes recursive event-triggering point process
Develops modified EM estimation algorithm
Presents simulation error injection framework
Differentiates primary and propagated errors
Models redundant multi-module perception systems

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Citation

@article{pan2024reliability, title={Reliability modeling for perception systems in autonomous vehicles: A recursive event-triggering point process approach}, author={Pan, Fenglian and Zhang, Yinwei and Liu, Jian and Head, Larry and Elli, Maria and Alvarez, Ignacio}, journal={Transportation Research Part C: Emerging Technologies}, volume={169}, pages={104868}, year={2024}, publisher={Elsevier} }

Pan, Fenglian and Zhang, Yinwei and Liu, Jian and Head, Larry and Elli, Maria and Alvarez, Ignacio (2024). Reliability modeling for perception systems in autonomous vehicles: A recursive event-triggering point process approach. Transportation Research Part C: Emerging Technologies.

talks

Challenges and Opportunities in Automated Driving in the Era of Agentic AI

I recently had the pleasure of speaking to the students of the Máster de Movilidad Cooperativa Conectada y Autónoma at Universidad Carlos III de Madrid, sharing how Generative AI is reshaping every corner of automated‑driving R&D. My session, “Challenges and Opportunities in Automated Driving in the Era of Agentic AI,” argued that these new AI models are far more than hype, they mark a genuine shift in the way we design, test, and ultimately will trust autonomous vehicles. Here are a few highlights from the talk content.

Generative AI Landscape

I started by defining what Generative AI means to me. I find that these terms are often overused, and end up meaning different things to different people. In my mind, Generative AI is a subset of Deep Learning, a group of algorithms that create new data rather than merely classify existing inputs. Whether through GANs, VAEs, diffusion pipelines, Transformers, or state‑space networks, these algorithms learn rich distributions of data representations from enormous data corpora and when trained, they become foundation models such as GPT‑n. Unlike hand‑coded software, they’re trained statistical models that approximate facets of human cognition. Based on the type of data they were trained we will have multiple “flavors” of foundation models with different capabilities.

So LLMs are not computer programs, they are “models”. Here is an analogy I like: if a computer program is like a recipe with precise steps, a model, is like having a chef who has learned to cook by tasting countless dishes and understanding flavor combinations. You can ask this “chef” to create new dishes based on this data-driven (learned) understanding, even if they haven’t seen that exact recipe before.

But LLMs are made of code. The “program” in this case is the architecture of the neural network and the learning algorithm, while the “model” is the resulting set of learned weights and biases within that network.

Generative AI cook

After getting our basis, I turned to the autonomous‑vehicle (AV) pipeline itself, where applications today cover the complete AV development stack. From simulation for realistic synthetic world generation as well as verification and validation, to perception problems, creating maps, predicting road user behaviors and new conversational AI assistants for passengers.

On simulation, I touched on solutions like Scenario Diffusion from Zoox that conjure complex, multi‑agent traffic scenes conditioned on HD maps and textual prompts, or UniSim by Waabi, which transforms mundane driving logs into editable, photorealistic worlds. These synthetic environments slash the expense and risk of road testing while letting engineers probe rare edge cases at scale.

On perception, I showcased DatasetGAN, an NVIDIA technique that simplifies manual labeling tasks and wiht a handful of labeled images spanws vast troves of perfectly segmented data.

One of the highlights on planning applicaitons is BehaviorGPT that impressed everyone with its ability to predict myriad plausible trajectories for every road user, capturing the uncertainty of traffic.

And as we get closer to the frontier of end‑to‑end decision making. DriveGPT4 shows how fused video perception with language‑model reasoning, can output low‑level control commands and natural‑language justifications for human consumption.

GenAI Pipeline

Of course, no talk is complete without a reality check. I emphasized the twin troubles of scarce, high‑quality multimodal data and the scary computational appetite of today’s large models. Achieving sub‑100‑millisecond latency for LLMs on in‑car hardware is no trivial feat. Security and safety are very real threats (think prompt‑injection attacks, IP leakage, nondeterministic outputs, and opaque reasoning) all of these challenge regulators and engineers alike.

Yet the opportunities are equally compelling. Model‑compression tricks like quantization, pruning, and distillation can turn bloated networks into nimble binaries, while neuromorphic chips such as Intel’s Loihi handle spike‑based inference with astonishing energy efficiency. Safety, too, can be engineered: binding language‑model suggestions inside a rigorously proven shell like Mobileye’s RSS keeps behaviors within an acceptable envelope, and tools like LVLM Interpret begin to open the model’s “black box,” offering visibility into why a recommendation was made.

Generative AI Landscape

I closed by reminding people that the march towards full autonomy is a marathon, but Generative AI has armed us with fresh answers to old questions. If we address data, performance, security, and safety head‑on, we can unlock a future of safer, more efficient, and more intelligent mobility. The technology is here to stay; our task now is to wield it ethically, safely, and wisely.

Thanks for reading! Contact me if you think the full content of this talk would be of interest to you or and audience of your choice.

—Prof. Ignacio Alvarez

Future of AI Transport

Ensuring driver alertness is a cornerstone of automotive safety, and Large Language Models (LLMs) offer a unique opportunity to create intelligent systems capable of detecting and mitigating sleepiness. By integrating multimodal inputs such as audio cues, video streams, and driving context signals, LLMs can process complex, real-time data to assess driver state and trigger appropriate actions to maintain alertness. In this project, students will design and prototype an automotive agent powered by LLMs to detect and respond to driver sleepiness. The system will utilize multimodal inputs, such as facial expressions, voice tone, and driving behavior, to compute a sleepiness likelihood metric. Based on the metric and additional contextual awareness signals (e.g., time of day, driving duration), the agent will propose tailored interventions, such as adjusting cabin temperature, suggesting a rest stop, or initiating engaging conversations. Students will evaluate the system’s usability, effectiveness, and user satisfaction, exploring the interplay between AI decision-making and driver interaction.

In this course, students designed and implemented a prototype in-vehicle game that incorporated input from an AV safety model, such as an AV agent equipped with RSS in the CARLA driving simulator to provide feedback on safety-related actions. Students evaluated their designed game for usability, engagement, and its effectiveness in increasing safety awareness in driving scenarios. Deliverables included a functional game prototype, an evaluation report, and insights into the application of gamification in AV safety education.

Student Project Details

Coming soon

Interested?

Contact me if you’d like me to teach this course to you or your audience.

Natural User Interfaces: Enhancing Autonomous Vehicle Safety Awareness through Gamification

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Description

Educating vehicle passengers and drivers about safety in autonomous vehicles (AVs) can be a challenge, especially when technical concepts like the Responsibility Sensitive Safety (RSS) model are involved. Gamification offers an innovative way to enhance user engagement and increase awareness of AV safety principles through the more natural interfaces of games by transforming complex concepts into interactive and enjoyable experiences. Integrating a safety model like RSS into a game can provide users with real-time feedback on safe driving practices and decision-making in various scenarios, as well as improve overall situational awareness during non-driving tasks.

In this course, students designed and implemented a prototype in-vehicle game that incorporated input from an AV safety model, such as an AV agent equipped with RSS in the CARLA driving simulator to provide feedback on safety-related actions. Students evaluated their designed game for usability, engagement, and its effectiveness in increasing safety awareness in driving scenarios. Deliverables included a functional game prototype, an evaluation report, and insights into the application of gamification in AV safety education.

Student Project Details

Coming soon

Interested?

Contact me if you’d like me to teach this course to you or your audience.

Vibe Coding for UX Designers

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Description

This course provides a practical introduction to the integration of generative AI and “vibe coding” into UX work-flows. Students will: • Explore foundational concepts of Generative AI, focusing on LLMs. • Learn techniques of prompt engineering to effectively direct AI tools. • Apply AI-driven methods to enhance UX research processes, from data collection to synthesis and prototyping. • Utilize AI-assisted development platforms, such as ChatGPT, Replit or Cursor, to prototype interactive user interfaces, tools and applications. • Investigate the emerging paradigm of Agentic AI and its potential to autonomously resolve user experience challenges. • Undertake a comprehensive final project, culminating in designing, building, and deploying a specialized AI agent addressing a defined UX issue.

Student Project Details

Coming soon

Interested?

Contact me if you’d like me to teach this course to you or your audience.

CV

Education

2012 International Ph.D in Computer Science, University of the Basque Country & Clemson University
- Supervisors: Karmele Lopez Ipina & Juan E. Gilbert
- Graduated Summa Cum Laude
2011 Advance Graduate Research in Engineering, University of Burgos
- Graduated Summa Cum Laude
2006 B.S. in Communication Sciences, University of Burgos
- Graduated Summa Cum Laude

Work experience

01/2025 - Present: Profesor of Human-centered Intelligent Systems
- THI, Computer Science Faculty, AI Motion Institute
12/2021 - Present: Principal Engineer, Technical Assistant to Intel Labs Director
- Intel Corporation, Intel Labs, Autonomous Driving Research
09/2014 - 12/2021: Sr. Research Scientist
- Intel Corporation, Intel Labs, Autonomous Driving Research
11/2013 - 09/2014 : Sr. Manager, IT Projects
- BMW China
12/2012 - 11/2013 : Manager, Connected Drive Asia Pacific
- BMW China
02/2012 - 12/2012 : IT Architect
- BMW AG
09/2009 - 02/2012 : Research Associate
- BMW USA, Greenville, SC
09/2009 - 02/2012 : Graudate Research Assistant
- Clemson University, School of Computing

Skills

Research
- Automotive
- Automated Driving
- Advance Driving Assistance Systems
- Safety
- Perception
- Planning
- Connected Vehicles - V2X
- Human Factors
- Human-Machine Interactions
- Driving Monitoring Systems
Technical
- Programming
- Data Analytics
- Product Development
- Prototyping
- Problem Solving
Languages
- Spanish - Native
- English / German - Fluent
- Chinese - Basic

Publications

Voice interfaced vehicle user help

Ignacio Alvarez et al.. (2010). Voice interfaced vehicle user help. Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

Are educational video games all they are cracked up to be

Ignacio Alvarez et al.. (2011). Are educational video games all they are cracked up to be published in International Journal of Learning Technology

Autonui a workshop on automotive natural user interfaces

Ignacio Alvarez et al.. (2011). Autonui a workshop on automotive natural user interfaces.in Autoui 2011

Designing driver centric natural voice user interfaces

Ignacio Alvarez et al.. (2011). Designing driver centric natural voice user interfaces. Adjunct Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications. online.

iHelp, the Ubiquitous Vehicle User Help

Ignacio Alvarez, Hans-Peter Fischer. (2011). iHelp, the Ubiquitous Vehicle User Help. Proceedings of Interact 2011.

AutoNUI: 2nd Workshop on Automotive Natural User Interfaces

Ignacio Alvarez et al.. (2012). AutoNUI: 2nd Workshop on Automotive Natural User Interfaces.Adjunct Proceedings of the 4th International Conference on Automotive User Interfaces

Contribution to the development of intelligent conversational assistants in automotive environments

Ignacio Javier Álvarez Martínez. (2012). Contribution to the development of intelligent conversational assistants in automotive environments. EHU.

Emotional Adaptive Vehicle User Interfaces, Moderating negative effects of failed technology interactions while driving

Ignacio Alvarez et al.. (2012). Emotional Adaptive Vehicle User Interfaces: moderating negative effects of failed technology interactions while driving. Adjunct Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI 2012).

Evaluating the ergonomics of BCI devices for research and experimentation

Joshua I Ekandem et al.. (2012). Evaluating the ergonomics of BCI devices for research and experimentation. Ergonomics.

The voice user help, a smart vehicle assistant for the elderly

Ignacio Alvarez, Miren Karmele López-de-Ipiña, Juan E Gilbert. (2012). The voice user help, a smart vehicle assistant for the elderly. International Conference on Ubiquitous Computing and Ambient Intelligence.

Response to Letter by Padulo and Ardigò

Joshua I Ekandem et al.. (2014). Response to Letter by Padulo and Ardigò. Ergonomics.

Social, natural, and peripheral interactions: Together and separate

Ignacio Alvarez et al.. (2014). Social, natural, and peripheral interactions: Together and separate. Proceedings of AutomotiveUI 2014.

The Social Car: Socially-inspired Mechanisms for Future Mobility Services

Ignacio Alvarez et al.. (2014). The Social Car: Socially-inspired Mechanisms for Future Mobility Services. Pervasive and Mobile Computing.

Help on the road: Effects of vehicle manual consultation in driving performance across modalities

Ignacio Alvarez et al.. (2015). Help on the road: Effects of vehicle manual consultation in driving performance across modalities. International journal of human-computer studies.

Prototyping adaptive automotive UX: A design pedagogy approach

Ignacio Alvarez et al.. (2015). Prototyping adaptive automotive UX: A design pedagogy approach. Extended Proceedings of the 7th International Conference on Automotive User.

Report on the in-vehicle auditory interactions workshop: Taxonomy, challenges, and approaches

Ignacio Alvarez et al.. (2015). Report on the in-vehicle auditory interactions workshop: Taxonomy, challenges, and approaches. Adjunct Proceedings AutomotiveUI ’15.

Skyline: a rapid prototyping driving simulator for user experience

Ignacio Alvarez et al.. (2015). Skyline: a rapid prototyping driving simulator for user experience. AutomotiveUI 2015.

Workshop on Adaptive Ambient In-Vehicle Displays and Interactions

Andreas Löcken et al.. (2015). Workshop on Adaptive Ambient In-Vehicle Displays and Interactions. Automotive UI 2015.

Workshop on Practical Experiences in Measuring and Modeling Drivers and Driver-Vehicle Interactions

Andreas Riener et al.. (2015). Workshop on Practical Experiences in Measuring and Modeling Drivers and Driver-Vehicle Interactions. Automotive UI 2015.

1st workshop on ethically inspired user interfaces for automated driving

Andreas Riener et al.. (2016). 1st workshop on ethically inspired user interfaces for automated driving. Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

1st workshop on situational awareness in semi-automated vehicles

Rod McCall et al.. (2016). 1st workshop on situational awareness in semi-automated vehicles. Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

Automotive user interfaces in the age of automation (Dagstuhl Seminar 16262)

Andreas Riener, Susanne Boll, Andrew L Kun. (2016). Automotive user interfaces in the age of automation (Dagstuhl Seminar 16262). Dagstuhl reports.

Autonomous hmi made easy: Prototyping reactive in-cabin aware hmis

Ignacio Alvarez et al.. (2016). Autonomous hmi made easy: Prototyping reactive in-cabin aware hmis. Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’16).

9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proceedings

Ignacio Alvarez et al.. (2017). 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proceedings. Automotive UI 2017.

Driver in the loop: Best practices in automotive sensing and feedback mechanisms

Ignacio Alvarez et al.. (2017). Driver in the loop: Best practices in automotive sensing and feedback mechanisms. Automotive user interfaces: creating interactive experiences in the car.

Skyline: A Platform Towards Scalable UX-Centric In-Vehicle HMI Development

Ignacio Alvarez et al.. (2017). Skyline: A Platform Towards Scalable UX-Centric In-Vehicle HMI Development. International Journal of Mobile Human Computer Interaction (IJMHCI).

Socializing under the influence of distracted driving: a study of the effects of in-vehicle and outside-of-the-vehicle communication while driving

Ignacio Alvarez et al.. (2017). Socializing under the influence of distracted driving: a study of the effects of in-vehicle and outside-of-the-vehicle communication while driving. Springer Nature Advances in Human Aspects of Transportation.

The insight–prototype–product cycle best practices and processes to iteratively advance in-vehicle interactive experiences development

Ignacio Alvarez et al.. (2017). The insight–prototype–product cycle best practices and processes to iteratively advance in-vehicle interactive experiences development. Automotive User Interfaces: Creating Interactive Experiences in the Car.

Towards Adaptive Ambient In-Vehicle Displays and Interactions: Insights and Design Guidelines from the 2015 AutomotiveUI Dedicated Workshop

Ignacio Alvarez et al.. (2017). Towards Adaptive Ambient In-Vehicle Displays and Interactions: Insights and Design Guidelines from the 2015 AutomotiveUI Dedicated Workshop. Automotive User Interfaces: Creating Interactive Experiences in the Car.

Workshop on user-centered design for automated driving systems

Anna-Katharina Frison et al.. (2017). Workshop on user-centered design for automated driving systems. Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

An international survey on automated and electric vehicles: Austria, Germany, South Korea, and USA

Ignacio Alvarez et al.. (2018). An international survey on automated and electric vehicles: Austria, Germany, South Korea, and USA. Springer Nature.

ARV 2018: 2nd Workshop on Augmented Reality for Intelligent Vehicles

Ignacio Alvarez et al.. (2018). ARV 2018: 2nd Workshop on Augmented Reality for Intelligent Vehicles. Adjunct Proceedings of the 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’18).

AutomotiveUI

Ignacio Alvarez et al.. (2018). AutomotiveUI. Automotive UI 2018.

Chairs

Ignacio Alvarez et al.. (2018). Chairs. Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

Emotional GaRage: A workshop on in-car emotion recognition and regulation

Ignacio Alvarez et al.. (2018). Emotional GaRage: A workshop on in-car emotion recognition and regulation. Adjunct Proceedings of the 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications AutomotiveUI 2018.

“Peace of Mind”, An Experiential Safety Framework for Automated Driving Technology Interactions

Marina Strano et al.. (2018). “Peace of Mind”, An Experiential Safety Framework for Automated Driving Technology Interactions. 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

Theo, take a right… uh… left: Conversational Route Negotiations with Autonomous Driving Assistants

Ignacio Alvarez et al.. (2018). Theo, take a right... uh... left: Conversational Route Negotiations with Autonomous Driving Assistants. Adjunct Proceedings of the 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’18).

Towards understanding emotional reactions of driver-passenger dyads in automated driving

Nese Alyuz et al.. (2018). Towards understanding emotional reactions of driver-passenger dyads in automated driving. 13th IEEE International Conference on Automatic Face & Gesture Recognition.

Workshop on designing highly automated driving systems as radical innovation

Anna-Katharina Frison et al.. (2018). Workshop on designing highly automated driving systems as radical innovation. Adjunct Proceedings of the 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’18).

2nd special session on solving the automated vehicle safety assurance challenge

Maria Soledad Elli et al.. (2019). 2nd special session on solving the automated vehicle safety assurance challenge. International Conference on Intelligent Transportation Systems (ITSC).

Design of a misbehavior detection system for objects based shared perception V2X applications

Ignacio Alvarez et al.. (2019). Design of a misbehavior detection system for objects based shared perception V2X applications. IEEE Intelligent Transportation Systems Conference.

Human-system cooperation in automated driving

Francesco Biondi, Ignacio Alvarez, Kyeong-Ah Jeong. (2019). Human-system cooperation in automated driving. No venue listed.

Human–vehicle cooperation in automated driving: A multidisciplinary review and appraisal

Ignacio Alvarez et al.. (2019). Human–vehicle cooperation in automated driving: A multidisciplinary review and appraisal. International Journal of Human Computer Interaction.

Object-level perception sharing among connected vehicles

Moreno Ambrosin et al.. (2019). Object-level perception sharing among connected vehicles. 2019 IEEE Intelligent Transportation Systems Conference (ITSC).

The SKYNIVI experience: evoking startle and frustration in dyads and single drivers

Ignacio Alvarez et al.. (2019). The SKYNIVI experience: evoking startle and frustration in dyads and single drivers. Proceedings of the IEEE International Conference in Intelligent Trasnportations Systems 2019.

Towards standardization of AV safety: C++ library for responsibility sensitive safety

Ignacio Alvarez et al.. (2019). Towards standardization of AV safety: C++ library for responsibility sensitive safety. Proceedings of the IEEE Intelligent Vehicle Symposium 2019.

Agents, environments, scenarios: A framework for examining models and simulations of human-vehicle interaction

Ignacio Alvarez et al.. (2020). Agents, environments, scenarios: A framework for examining models and simulations of human-vehicle interaction. Transportation research interdisciplinary perspectives.

Auto-UI Global Perspectives

Ignacio Alvarez et al.. (2020). Auto-UI Global Perspectives. ACM IX Interaction Journal.

Emotion GaRage Vol. II A Workshop on Affective In-Vehicle Display Design

Ignacio Alvarez et al.. (2020). Emotion GaRage Vol. II A Workshop on Affective In-Vehicle Display Design. Proceedings of 12th International Conference in Automotive User Interfaces and Interactive Vehicular Applications.

Evaluation of responsibility-sensitive safety (rss) model based on human-in-the-loop driving simulation

Chen Chai et al.. (2020). Evaluation of responsibility-sensitive safety (rss) model based on human-in-the-loop driving simulation. In proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems.

How safe is safe enough? Automatic safety constraints boundary estimation for decision-making in automated vehicles

Alëna Rodionova et al.. (2020). How safe is safe enough? Automatic safety constraints boundary estimation for decision-making in automated vehicles. Proceedings of IEEE Intelligent Vehicle Symposium 2020.

PURSS: Towards Perceptual Uncertainty Aware Responsibility Sensitive Safety with ML.

Ignacio Alvarez et al.. (2020). PURSS: Towards Perceptual Uncertainty Aware Responsibility Sensitive Safety with ML.. Proceedings of 2020 AAAI Conference.

MISO-V: Misbehavior detection for collective perception services in vehicular communications

Ignacio Alvarez et al.. (2021). MISO-V: Misbehavior detection for collective perception services in vehicular communications. Proceedings of IEEE Intelligent Transportation Systems Conference 2021.

On responsibility sensitive safety in car-following situations-a parameter analysis on german highways

Maximilian Naumann et al.. (2021). On responsibility sensitive safety in car-following situations-a parameter analysis on german highways. In 2021 Proceedings of IEEE Intelligent Vehicles Symposium (IV).

To err is human: The role of human derived safety metrics in an age of automated vehicles

Ignacio Alvarez et al.. (2021). To err is human: The role of human derived safety metrics in an age of automated vehicles. SAE Technical Paper.

Driver emotions in automated vehicles

Shabnam FakhrHosseini et al.. (2022). Driver emotions in automated vehicles. User Experience Design in the Era of Automated Driving.

Emotion GaRage Vol. III: A Workshop on Affective In-Vehicle Display Applications

Ignacio Alvarez et al.. (2022). Emotion GaRage Vol. III: A Workshop on Affective In-Vehicle Display Applications. Adjunct Proceedings of the 14th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

Empathic vehicle design: Use cases and design directions from two workshops

Ignacio Alvarez et al.. (2022). Empathic vehicle design: Use cases and design directions from two workshops. CHI 2022.

IEEE Standard for Assumptions in Safety-Related Models for Automated Driving Systems

ITS IEEE VT et al.. (2022). IEEE Standard for Assumptions in Safety-Related Models for Automated Driving Systems. No venue listed.

Quantifying Error Propagation in Multi-Stage Perception System of Autonomous Vehicles via Physics-Based Simulation

Fenglian Pan et al.. (2022). Quantifying Error Propagation in Multi-Stage Perception System of Autonomous Vehicles via Physics-Based Simulation. Proceedings of the IEEE 2022 Winter Simulation Conference.

Special Issue

Ignacio Alvarez et al.. (2022). Special Issue. MDPI Sensors.

User experience design in the era of automated driving

Andreas Riener, Myounghoon Jeon, Ignacio Alvarez. (2022). User experience design in the era of automated driving. Springer Studies in Computational 980.

White Paper-Literature Review on Kinematic Properties of Road Users for Use on Safety-Related Models for Automated Driving Systems

ITS IEEE VT et al.. (2022). White Paper-Literature Review on Kinematic Properties of Road Users for Use on Safety-Related Models for Automated Driving Systems. IEEE Standards.

Application of Responsibility-Sensitive Safety in Areas with Limited Visibility: Occlusions in RSS

Ignacio Alvarez et al.. (2023). Application of Responsibility-Sensitive Safety in Areas with Limited Visibility: Occlusions in RSS. 2023 IEEE 26th International Conference on Intelligent Transportation Systems.

Emotion GaRage Vol. IV: Creating empathic in-vehicle interfaces with generative AIs for automated vehicle contexts

Ignacio Alvarez et al.. (2023). Emotion GaRage Vol. IV: Creating empathic in-vehicle interfaces with generative AIs for automated vehicle contexts. Proceedings of Automotive UI 2023.

“Play Your Anger”: A report on the empathic in-vehicle interface workshop

Ignacio Alvarez et al.. (2023). “Play Your Anger”: A report on the empathic in-vehicle interface workshop. Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

RSS Demonstrator: a Tool for User Experience Interactions with Automated Driving Safety Models

Ignacio Alvarez et al.. (2023). RSS Demonstrator: a Tool for User Experience Interactions with Automated Driving Safety Models. Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

Inside Out: Emotion GaRage Vol. V

Ignacio Alvarez et al.. (2024). Inside Out: Emotion GaRage Vol. V. Adjunct Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

Reliability modeling for perception systems in autonomous vehicles: A recursive event-triggering point process approach

Ignacio Alvarez et al.. (2024). Reliability modeling for perception systems in autonomous vehicles: A recursive event-triggering point process approach. Transportation Research Part C Emerging Technologies.

Talks

Challenges and Opportunities in Automated Driving in the Era of Agentic AI

May 23, 2025

University Invited Lecture at Universidad Carlos 3 Madrid (UCM3), Master de Movilidad Cooperative Conectada, Madrid, Spain

Teaching

Study design and implementation of UX tests: AV harrassment

Study design and implementation of UX tests: Emphatic Car

Artificial Intelligence: LLM-based Agent for Driver Sleepiness Detection and Mitigation in Automotive Systems

Natural User Interfaces: Enhancing Autonomous Vehicle Safety Awareness through Gamification

Vibe Coding for UX Designers

Service and leadership

IEEE Sr. Member
ACM Member
IJHCI - Board Member
AutoUI - SC Member
IEEE ITSS - Member

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.moon-icon{display:none}[data-theme="dark"] .theme-toggle .sun-icon{display:inline}.author__urls .fa-map-marker{color:#e74c3c !important}[data-theme="dark"] .author__urls .fa-github,[data-theme="dark"] .author__urls .fab.fa-github{color:#ffffff !important}.author__urls .fa-github,.author__urls .fab.fa-github{color:#171516 !important}.author__urls .fa-map-marker{color:#e74c3c !important}.social-icons .fa-github,.social-icons .fab.fa-github{color:#171516 !important}[data-theme="dark"] .social-icons .fa-github,[data-theme="dark"] .social-icons .fab.fa-github{color:#ffffff !important}

Markdown

Locations of key files/directories

Basic config options: _config.yml
Top navigation bar config: _data/navigation.yml
Single pages: _pages/
Collections of pages are .md or .html files in:
- _publications/
- _portfolio/
- _posts/
- _teaching/
- _talks/
Footer: _includes/footer.html
Static files (like PDFs): /files/
Profile image (can set in _config.yml): images/profile.png

Tips and hints

Name a file “.md” to have it render in markdown, name it “.html” to render in HTML.
Go to the commit list (on your repo) to find the last version Github built with Jekyll.
- Green check: successful build
- Orange circle: building
- Red X: error
- No icon: not built

Resources

Liquid syntax guide

Markdown guide

Header three

Header four

Header five

Header six

Blockquotes

Single line blockquote:

Quotes are cool.

Tables

Table 1

Entry	Item
John Doe	2016	Description of the item in the list
Jane Doe	2019	Description of the item in the list
Doe Doe	2022	Description of the item in the list

Table 2

Header1	Header2	Header3
cell1	cell2	cell3
cell4	cell5	cell6
cell1	cell2	cell3
cell4	cell5	cell6
Foot1	Foot2	Foot3

Definition Lists

Definition List Title: Definition list division.
Startup: A startup company or startup is a company or temporary organization designed to search for a repeatable and scalable business model.
#dowork: Coined by Rob Dyrdek and his personal body guard Christopher “Big Black” Boykins, “Do Work” works as a self motivator, to motivating your friends.
Do It Live: I’ll let Bill O’Reilly explain this one.

Unordered Lists (Nested)

List item one
- List item one
  - List item one
  - List item two
  - List item three
  - List item four
- List item two
- List item three
- List item four
List item two
List item three
List item four

Ordered List (Nested)

List item one
1. List item one
  1. List item one
  2. List item two
  3. List item three
  4. List item four
2. List item two
3. List item three
4. List item four
List item two
List item three
List item four

Buttons

Make any link standout more when applying the .btn class.

Notices

Watch out! You can also add notices by appending {: .notice} to a paragraph.

HTML Tags

Address Tag

1 Infinite Loop
Cupertino, CA 95014
United States

Anchor Tag (aka. Link)

This is an example of a link.

Abbreviation Tag

The abbreviation CSS stands for “Cascading Style Sheets”.

Cite Tag

“Code is poetry.” —Automattic

Code Tag

You will learn later on in these tests that word-wrap: break-word; will be your best friend.

Strike Tag

This tag will let you ~~strikeout text~~.

Emphasize Tag

The emphasize tag should italicize text.

Insert Tag

This tag should denote inserted text.

Keyboard Tag

This scarcely known tag emulates keyboard text, which is usually styled like the <code> tag.

Preformatted Tag

This tag styles large blocks of code.

.post-title {
  margin: 0 0 5px;
  font-weight: bold;
  font-size: 38px;
  line-height: 1.2;
  and here's a line of some really, really, really, really long text, just to see how the PRE tag handles it and to find out how it overflows;
}

Quote Tag

Developers, developers, developers… –Steve Ballmer

Strong Tag

This tag shows bold text.

Subscript Tag

Getting our science styling on with H₂O, which should push the “2” down.

Superscript Tag

Still sticking with science and Isaac Newton’s E = MC², which should lift the 2 up.

Variable Tag

This allows you to denote variables.

Page not in menu

This is a page not in the menu. You can use markdown in this page.

Heading 1

Heading 2

Page Archive

{% include base_path %} {% for post in site.pages %} {% include archive-single.html %} {% endfor %}

Patents

{% include base_path %} {% assign postsByYear = site.patents | group_by_exp:"post", "post.date | date: '%Y'" | reverse %} {% for year_group in postsByYear %} <details class="year-details" {% if forloop.first %}open{% endif %}>

{%- comment -%} Using 'publications-list-indent' for consistency, can be renamed {%- endcomment -%} {% for post in year_group.items reversed %}

{%- comment -%} Using 'publication-item' for consistency, can be renamed {%- endcomment -%} {% include archive-single.html %}

{% endfor %}

</details> {% endfor %}

Portfolio

{% include base_path %} {% for post in site.portfolio %} {% include archive-single.html %} {% endfor %}

Publications

{% if author.googlescholar %} You can also find my articles on my Google Scholar profile. {% endif %} {% include base_path %} {% assign postsByYear = site.publications | group_by_exp:"post", "post.date | date: '%Y'" | reverse %} {% for year_group in postsByYear %} <details class="year-details" {% if forloop.first %}open{% endif %}>

{% for post in year_group.items reversed %}

{% include archive-single.html %}

{% endfor %}

</details> {% endfor %}

Executive Portfolio

IGNACIO ALVAREZ

Technology Executive | Automotive & AI Leader

📍 Based in Germany · Open to Relocation Globally

ignacio.alvarez@thi.de LinkedIn

Download PDF Resume Listen Resume Podcast

Executive Summary

Technology Executive with a track record of CTO-level impact in the Automotive & AI sectors. Proven leader with 15+ years of experience architecting and scaling intelligent systems from R&D to series production, driving 30% YoY R&D ROI amidst budget reductions, leading global R&D organizations of 100+ engineers, and launching platforms adopted by 1M+ users. Expert in defining technical vision that aligns with C-suite objectives, secures market leadership, and generates massive IP value (150+ patents).

Core Competencies

Global R&D Leadership (100+ engineers) Technology Strategy & Roadmaps Productization & Tech Transfer Automated Driving Architecture Compute Platform Strategy Artificial Intelligence & Machine Learning Deep Learning & Generative AI Functional Safety Connected Vehicles (V2X) User-Centered Design Human–Machine Interface (HMI) Cyber-Physical Security Digital Twins & Large Scale Simulation Edge AI & Perception Stacks Behavior/Planning Verification & Validation Connected Vehicle Services Standards Leadership & Ecosystem Patent Strategy & Portfolio Development Cross-Functional Leadership Fluent in English, Spanish & German Basic Mandarin

IEEE Intelligent Transportation Systems Society – Member of Board of Governors (elected role driving global ITS strategy)

ACM AutomotiveUI Conference – Steering Committee Member (shaping annual agenda for automotive UX innovation)

Editorial Boards – Board Member for IEEE ITS Magazine and Int. Journal of Human-Computer Interaction

Senior Member, IEEE and Member, ACM, SAE (recognized professional standing and contributions in tech)

Thought Leadership & Innovation

Patents

Holder of 150+ patents (50+ granted) in autonomous systems, AI, and robotics, shaping the core IP strategy for market-leading mobility solutions.

View Patent Portfolio

Publications

Author of 70+ publications in top-tier IEEE and ACM venues and editor of multiple books on autonomous system engineering.

View Publications

Industry Standards

Key contributor to the definition of global safety and communication standards for autonomous vehicles through leadership roles in IEEE and ETSI.

View Standards Work

Education

Ph.D. in Computer Science (Automotive Intelligent Systems)

University of the Basque Country, Spain & Clemson University, USA, 2012

Summa Cum Laude

Joint international doctoral program focusing on conversational AI for vehicles.

M.Sc. in Media and Information Technology

Hochschule Offenburg, Germany, 2009

Coursework in multimedia systems and human-computer interaction.

B.Sc. in Communication Sciences

University of Burgos, Spain, 2006

Summa Cum Laude

Minor in Telecommunications.

Sitemap

{% include base_path %} A list of all the posts and pages found on the site. For you robots out there is an [XML version]({{ base_path }}/sitemap.xml) available for digesting as well.

Pages

{% for post in site.pages %} {% include archive-single.html %} {% endfor %}

Posts

{% for post in site.posts %} {% include archive-single.html %} {% endfor %} {% capture written_label %}'None'{% endcapture %} {% for collection in site.collections %} {% unless collection.output == false or collection.label == "posts" %} {% capture label %}{{ collection.label }}{% endcapture %} {% if label != written_label %}

{% capture written_label %}{{ label }}{% endcapture %} {% endif %} {% endunless %} {% for post in collection.docs %} {% unless collection.output == false or collection.label == "posts" %} {% include archive-single.html %} {% endunless %} {% endfor %} {% endfor %}

Posts by Tags

{% include base_path %} {% include group-by-array collection=site.posts field="tags" %} {% for tag in group_names %} {% assign posts = group_items[forloop.index0] %}

{% for post in posts %} {% include archive-single.html %} {% endfor %} {% endfor %}

Talk map

This map is generated from a Jupyter Notebook file in /_talks/talkmap.ipynb, which mines the location fields in the .md files in _talks/.

Talks and presentations

{% if site.talkmap_link == true %}

See a map of all the places I've given a talk!

{% endif %} {% for post in site.talks reversed %} {% include archive-single-talk.html type='talk' %} {% endfor %}

Talks

{% include base_path %} {% assign postsByYear = site.talks | group_by_exp:"post", "post.date | date: '%Y'" | reverse %} {% for year_group in postsByYear %} <details class="year-details" {% if forloop.first %}open{% endif %}>

{% for post in year_group.items reversed %}

{% include archive-single-talk.html type='talk' %}

{% endfor %}

</details> {% endfor %}

Teaching

{% include base_path %} {% for post in site.teaching reversed %} {% include archive-single.html %} {% endfor %}

Teaching

{% include base_path %} {% assign postsByYear = site.teaching | group_by_exp:"post", "post.date | date: '%Y'" | reverse %} {% for year_group in postsByYear %} <details class="year-details" {% if forloop.first %}open{% endif %}>

{% for post in year_group.items reversed %}

{% include archive-single-teaching.html type='teaching' %}

{% endfor %}

</details> {% endfor %}

Terms and Privacy Policy

{% include base_path %} {% include toc %} ## Privacy Policy The privacy of my visitors is extremely important. This Privacy Policy outlines the types of personal information that is received and collected and how it is used. First and foremost, I will never share your email address or any other personal information to anyone without your direct consent. ### Log Files Like many other websites, this site uses log files to help learn about when, from where, and how often traffic flows to this site. The information in these log files include: * Internet Protocol addresses (IP) * Types of browser * Internet Service Provider (ISP) * Date and time stamp * Referring and exit pages * Number of clicks All of this information is not linked to anything that is personally identifiable. ### Cookies and Web Beacons When you visit this site "convenience" cookies are stored on your computer when you submit a comment to help you log in faster to [Disqus](http://disqus.com) the next time you leave a comment. Third-party advertisers may also place and read cookies on your browser and/or use web beacons to collect information. This site has no access or control over these cookies. You should review the respective privacy policies on any and all third-party ad servers for more information regarding their practices and how to opt-out. If you wish to disable cookies, you may do so through your web browser options. Instructions for doing so can be found on the specific web browsers' websites. #### Google Analytics Google Analytics is a web analytics tool I use to help understand how visitors engage with this website. It reports website trends using cookies and web beacons without identifying individual visitors. You can read [Google Analytics Privacy Policy](http://www.google.com/analytics/learn/privacy.html).

Blog posts

{% include base_path %} {% capture written_year %}'None'{% endcapture %} {% for post in site.posts %} {% capture year %}{{ post.date | date: '%Y' }}{% endcapture %} {% if year != written_year %}

{% capture written_year %}{{ year }}{% endcapture %} {% endif %} {% include archive-single.html %} {% endfor %}

@import "jekyll-theme-primer";

{"/about/":"https://ignacioalvmar.com/","/about.html":"https://ignacioalvmar.com/","/md/":"https://ignacioalvmar.com/markdown/","/markdown.html":"https://ignacioalvmar.com/markdown/","/nmp/":"https://ignacioalvmar.com/non-menu-page/","/nmp.html":"https://ignacioalvmar.com/non-menu-page/","/cv/":"https://ignacioalvmar.com/resume/","/wordpress/blog-posts/":"https://ignacioalvmar.com/year-archive/"}

Jupyter notebook markdown generator

# Jupyter notebook markdown generator These .ipynb files are Jupyter notebook files that convert a TSV containing structured data about talks (`talks.tsv`) or presentations (`presentations.tsv`) into individual markdown files that will be properly formatted for the academicpages template. The notebooks contain a lot of documentation about the process. The .py files are pure python that do the same things if they are executed in a terminal, they just don't have pretty documentation.

{% if page.xsl %} {% endif %} {% assign collections = site.collections | where_exp:'collection','collection.output != false' %}{% for collection in collections %}{% assign docs = collection.docs | where_exp:'doc','doc.sitemap != false' %}{% for doc in docs %} {{ doc.url | replace:'/index.html','/' | absolute_url | xml_escape }} {% if doc.last_modified_at or doc.date %}{{ doc.last_modified_at | default: doc.date | date_to_xmlschema }} {% endif %} {% endfor %}{% endfor %}{% assign pages = site.html_pages | where_exp:'doc','doc.sitemap != false' | where_exp:'doc','doc.url != "/404.html"' %}{% for page in pages %} {{ page.url | replace:'/index.html','/' | absolute_url | xml_escape }} {% if page.last_modified_at %}{{ page.last_modified_at | date_to_xmlschema }} {% endif %} {% endfor %}{% assign static_files = page.static_files | where_exp:'page','page.sitemap != false' | where_exp:'page','page.name != "404.html"' %}{% for file in static_files %} {{ file.path | replace:'/index.html','/' | absolute_url | xml_escape }} {{ file.modified_time | date_to_xmlschema }} {% endfor %}

Sitemap: {{ "sitemap.xml" | absolute_url }}

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Patents

Access paper here

Autonomous vehicle perception multimodal sensor data management

Published in: Patent Office Authors: Julio Fernando Jarquin Arroyo et al. Citations: 1

Liuyang Yang et al.

No venue listed (2024)

PDF

BibTeX

@article{liuyang2024misbehaviordete,
  author    = {Liuyang Yang et al.},
  title     = {Misbehavior detection using data consistency checks for collective perception messages},
  journal  = {No venue listed},
  year      = {2024}
}

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Publications

2024

Reliability modeling for perception systems in autonomous vehicles: A recursive event-triggering point process approach

Published in ‘Transportation Research Part C: Emerging Technologies’, 2024

Abstract

Ensuring the reliability of sensor-fusion-based perception systems is crucial for the safe deployment of autonomous vehicles. Such systems function through a sequence of interconnected stages, where errors in upstream stages may propagate to downstream stages and trigger additional errors. The cross-stage error propagation conceptually exists and makes errors in different stages, not independent, posing model challenges, estimation challenges, and data challenges for reliability modeling. The existing methods cannot be applied to address all these challenges. Thus, this paper presents a recursive event-triggering point process to explicitly consider the error propagation based on the simulated data. The data are simulated from a proposed error injection framework, which can generate various errors from a sequence of interconnected stages in a perception system. The latent and probabilistic error propagation information is incorporated into a modified expectation–maximization (EM) algorithm for parameter estimation. The numerical and physics-based simulation case studies demonstrate the prediction accuracy and interpretability of the proposed modeling methodology.

Key Contributions

Automated driving system development
Proposes recursive event-triggering point process
Develops modified EM estimation algorithm
Presents simulation error injection framework
Differentiates primary and propagated errors
Models redundant multi-module perception systems

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Citation

@article{pan2024reliability, title={Reliability modeling for perception systems in autonomous vehicles: A recursive event-triggering point process approach}, author={Pan, Fenglian and Zhang, Yinwei and Liu, Jian and Head, Larry and Elli, Maria and Alvarez, Ignacio}, journal={Transportation Research Part C: Emerging Technologies}, volume={169}, pages={104868}, year={2024}, publisher={Elsevier} }

Pan, Fenglian and Zhang, Yinwei and Liu, Jian and Head, Larry and Elli, Maria and Alvarez, Ignacio (2024). Reliability modeling for perception systems in autonomous vehicles: A recursive event-triggering point process approach. Transportation Research Part C: Emerging Technologies.

Inside Out: Emotion GaRage Vol. V

Published in ‘Adjunct Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications’, 2024

Abstract

The rapid advancement of automated vehicles has aroused the curiosity of researchers in the automotive field. Understanding the emotional aspects of this technology is critical to improving human-vehicle interactions. The topics of the proposed workshop will be expanded from internal to external empathetic interface designs of automated vehicles. The workshop will gather researchers and practitioners to brainstorm and design affective internal and external interfaces for automated vehicles, targeting specific use cases within the social context. During the workshop, participants will use an affective design tool and generative AI to prototype affective interface designs in automated vehicles. With this creative approach, we aim to expand the knowledge of affective eHMIs in addition to in-vehicle designs and understand social factors that contribute to the user perceptions of automated vehicles.

Key Contributions

Emotional and affective computing
Expands focus to external vehicle interfaces
Aims to create design guidelines
Addresses social context and ethical issues
Prototype affective interface designs

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Citation

@inproceedings{10.1145/3641308.3677403, author = {Dong, Jiayuan and Gowda, Nikhil and Wang, Yiyuan and Choe, Mungyeong and Alsaid, Areen and Alvarez, Ignacio and Krome, Sven and Jeon, Myounghoon}, title = {Inside Out: Emotion GaRage Vol. V}, year = {2024}, isbn = {9798400705205}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3641308.3677403}, doi = {10.1145/3641308.3677403}, pages = {260–263}, numpages = {4}, keywords = {affective external human-machine interaction designs, emotions, empathic in-vehicle interfaces, generative artificial intelligence}, location = {Stanford, CA, USA}, series = {AutomotiveUI '24 Adjunct} }

Dong, Jiayuan and Gowda, Nikhil and Wang, Yiyuan and Choe, Mungyeong and Alsaid, Areen and Alvarez, Ignacio and Krome, Sven and Jeon, Myounghoon (2024). Inside Out: Emotion GaRage Vol. V. .

2023

RSS Demonstrator: a Tool for User Experience Interactions with Automated Driving Safety Models

Published in ‘Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications’, 2023

Abstract

Safety Assurance remains a challenge for the large-scale deployment of Automated Driving Systems (ADS). Safety models monitor the performance of the ADS. Most safety models are validated both in simulation and during on-road tests. However, first-hand experiences and analysis of ADS safety models are not easily accessible to the general research community. This paper introduces the RSS driving demonstrator an open-source simulation tool that enables first-hand experience of the Responsibility Sensitive Safety (RSS) safety model proposed by Intel and Mobileye and adopted by several Automotive Industry standards and regulatory frameworks. The RSS demonstrator enables first-hand interactions and experience of ADS safety model restrictions in both automated and manual driving conditions. As a User Experience (UX) tool, it provides quantitative safety metrics and flexible user interaction features. The results indicate it served to both evangelize the RSS ADS safety model with laymen population and is a versatile tool for Automotive UX development.

Key Contributions

Safety analysis and validation
User experience design and evaluation
Automated driving system development
Tool development and demonstration
Human-computer interaction design

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Citation

@inproceedings{10.1145/3581961.3609894, author = {Alvarez, Ignacio and Gassmann, Bernd and Pasch, Frederik and Oboril, Fabian}, title = {RSS Demonstrator: a Tool for User Experience Interactions with Automated Driving Safety Models}, year = {2023}, isbn = {9798400701122}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3581961.3609894}, doi = {10.1145/3581961.3609894}, pages = {180–185}, numpages = {6}, keywords = {Automated Driving Safety Model, Automated Driving System, Driving Simulation, RSS, Safety, User Experience}, location = {Ingolstadt, Germany}, series = {AutomotiveUI '23 Adjunct} }

Alvarez, Ignacio and Gassmann, Bernd and Pasch, Frederik and Oboril, Fabian (2023). RSS Demonstrator: a Tool for User Experience Interactions with Automated Driving Safety Models. .

“Play Your Anger”: A report on the empathic in-vehicle interface workshop

Published in ‘Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications’, 2023

Abstract

Empathic in-vehicle interfaces are critical in improving user safety and experiences. There has been much research on how to estimate drivers’ affective states, whereas little research has investigated intervention methods that mitigate potential impacts from the driver’s affective states on their driving performance and user experiences. To enhance the development of in-vehicle interfaces considering emotional aspects, we have organized a workshop series to gather automotive user interface experts to discuss this topic at the International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI). The present paper focuses particularly on the intervention methods created by the experts and proposes design recommendations for future empathic in-vehicle interfaces. We hope this work can spark lively discussions on the importance of drivers’ affective states in their user experience of automated vehicles and pose the right direction.

Key Contributions

Presents novel empathic interface prototypes.
Offers expert-driven design recommendations.
Addresses anger, frustration, and fear.
Details a three-phase design process.
Focuses on specific intervention methods.

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Citation

@inproceedings{10.1145/3581961.3609865, author = {Dong, Jiayuan and Nadri, Chihab and Alvarez, Ignacio and Diels, Cyriel and Lee, Myeongkyu and Li, Jingyi and Liao, Pei Hsuan and Manger, Carina and Sadeghian, Shadan and Schu\ss{}, Martina and Walker, Bruce N. and Walker, Francesco and Wang, Yiyuan and Jeon, Myounghoon}, title = {“Play Your Anger”: A Report on the Empathic In-vehicle Interface Workshop}, year = {2023}, isbn = {9798400701122}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3581961.3609865}, doi = {10.1145/3581961.3609865}, booktitle = {Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages = {168–173}, numpages = {6}, location = {Ingolstadt, Germany}, series = {AutomotiveUI '23 Adjunct} }

Dong, Jiayuan and Nadri, Chihab and Alvarez, Ignacio and Diels, Cyriel and Lee, Myeongkyu and Li, Jingyi and Liao, Pei Hsuan and Manger, Carina and Sadeghian, Shadan and Schu\ss{ (2023). “Play Your Anger”: A report on the empathic in-vehicle interface workshop. Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

Emotion GaRage Vol. IV: Creating empathic in-vehicle interfaces with generative AIs for automated vehicle contexts

Published in ‘Proceedings of ACM’s Automotive UI 2023’, ACM 2023

Abstract

This workshop aims to design advanced empathic user interfaces for in-vehicle displays, particularly for high-level automated vehicles (SAE level 3 or higher). Incorporating model-based approaches for understanding human emotion regulation, it seeks to enhance the user-vehicle interaction. A unique aspect of this workshop is the integration of generative artificial intelligence (AI) tools in the design process. The workshop will explore generative AI’s potential in crafting contextual responses and its impact on user experience and interface design. The agenda includes brainstorming on various driving scenarios, developing emotion-oriented intervention methods, and rapid prototyping with AI tools. The anticipated outcome includes practical prototypes of affective user interfaces and insights on the role of AI in designing human-machine interactions. Through this workshop, we hope to contribute to making automated driving more accessible and enjoyable.

Key Contributions

Automated driving system development
Emotional and affective computing
Designs advanced empathic user interfaces
Integrates generative AI in design process
Applies model-based emotion regulation methods

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Citation

@inproceedings{10.1145/3581961.3609828, author = {Choe, Mungyeong and Bosch, Esther and Dong, Jiayuan and Alvarez, Ignacio and Oehl, Michael and Jallais, Christophe and Alsaid, Areen and Nadri, Chihab and Jeon, Myounghoon}, title = {Emotion GaRage Vol. IV: Creating Empathic In-Vehicle Interfaces with Generative AIs for Automated Vehicle Contexts}, year = {2023}, isbn = {9798400701122}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3581961.3609828}, doi = {10.1145/3581961.3609828}, booktitle = {Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages = {234–236}, numpages = {3}, keywords = {ChatGPT, affective computing, emotions, empathic vehicles, interaction design}, location = {Ingolstadt, Germany}, series = {AutomotiveUI '23 Adjunct} }

Choe, Mungyeong and Bosch, Esther and Dong, Jiayuan and Alvarez, Ignacio and Oehl, Michael and Jallais, Christophe and Alsaid, Areen and Nadri, Chihab and Jeon, Myounghoon (2023). Emotion GaRage Vol. IV: Creating empathic in-vehicle interfaces with generative AIs for automated vehicle contexts. Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

Application of Responsibility-Sensitive Safety in Areas with Limited Visibility: Occlusions in RSS

Published in ‘2023 IEEE 26th International Conference on Intelligent Transportation Systems’, IEEE 2023

Abstract

Areas of limited visibility are common in day-to-day traffic: be it static buildings, parked vehicles, traffic participants blocking the vehicle’s line of sight, harsh weather conditions or just narrow curves that impede the automated driving sensor suite to inspect the road ahead. Autonomous vehicles have to be able to safely cope with this kind of constraints. The Responsibility-Sensitive Safety model (RSS) demands vehicles to exercise caution with respect to occlusions and to consider also occluded road agents. This paper provides a concrete implementation of how occlusions in RSS can be addressed and investigates the balance between safety and usefulness of the model when a reasonably foreseeable behavior of occluded road agents is assumed. We perform occlusion experiments in urban as well as on highway scenarios with the driving simulation platform CARLA applying different parameterization of the agents kinematic properties and the safety model parameters to analyse and judge the consequences with respect to safe driving and overcautious driving behaviors.

Key Contributions

Safety analysis and validation
Implements occlusion handling with artificial vehicles
Evaluates approach in urban/highway scenarios
Shows aggressive parameters can reduce usability
Analyzes real-world data for assumptions
Demonstrates collision mitigation via simulation

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Citation

@INPROCEEDINGS{10421912, author={Gassmann, Bernd and Dey, Shreya and Alvarez, Ignacio and Oboril, Fabian and Scholl, Kay-Ulrich}, booktitle={2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)}, title={Application of Responsibility-Sensitive Safety in Areas with Limited Visibility: Occlusions in RSS}, year={2023}, volume={}, number={}, pages={5976-5981}, keywords={Roads;Kinematics;Safety;Behavioral sciences;Usability;Intelligent transportation systems;Meteorology}, doi={10.1109/ITSC57777.2023.10421912} }

Gassmann, Bernd and Dey, Shreya and Alvarez, Ignacio and Oboril, Fabian and Scholl, Kay-Ulrich (2023). Application of Responsibility-Sensitive Safety in Areas with Limited Visibility: Occlusions in RSS. 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC).

2022

White Paper-Literature Review on Kinematic Properties of Road Users for Use on Safety-Related Models for Automated Driving Systems

Published in IEEE Standards, 2022

Abstract

This document presents a review of relevant literature (e.g., standards, regulations, and scientific publications) that investigated kinematic behavior of road users. This review is intended to serve as a key contribution to the Automated Driving Systems (ADS) research and industry communities, as well as to current standardization efforts, such as IEEE Std 2846, IEEE Standard for Assumptions in Safety-Related Models for Automated Driving Systems.

Key Contributions

Literature review road actors kinematic behavior
Systematic data organization of kinematic parameter values
Kinematic data contextualization
Research gap analysis

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Citation

@ARTICLE{9763462, author={IEEE VT, ITS}, journal={Literature Review on Kinematic Properties of Road Users for Use on Safety-Related Models for Automated Driving Systems}, title={Literature Review on Kinematic Properties of Road Users for Use on Safety-Related Models for Automated Driving Systems}, year={2022}, volume={}, number={}, pages={1-35}, keywords={ADS;automated driving systems;behaviors;IEEE 2846;kinematic;literature review;road users;standards;white paper}, doi={} }

Alvarez, I. (2022). . Literature Review on Kinematic Properties of Road Users for Use on Safety-Related Models for Automated Driving Systems.

User experience design in the era of automated driving

Published in Journal Name, 2023

Abstract

This book is dedicated to user experience design for automated driving to address humane aspects of automated driving, e.g., workload, safety, trust, ethics, and acceptance. Automated driving has experienced a major development boost in recent years. However, most of the research and implementation has been technology-driven, rather than human-centered. The levels of automated driving have been poorly defined and inconsistently used. A variety of application scenarios and restrictions has been ambiguous. Also, it deals with human factors, design practices and methods, as well as applications, such as multimodal infotainment, virtual reality, augmented reality, and interactions in and outside users. This book aims at 1) providing engineers, designers, and practitioners with a broad overview of the state-of-the-art user experience research in automated driving to speed-up the implementation of automated vehicles and 2) helping researchers and students benefit from various perspectives and approaches to generate new research ideas and conduct more integrated research.

Key Contributions

State-of-the-art UX Design in Automated Driving
Foundational theory compilation
UX design methods and frameworks
Interface application and case studies

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Citation

@book{riener2022user, title={User experience design in the era of automated driving}, author={Riener, Andreas and Jeon, Myounghoon and Alvarez, Ignacio}, volume={980}, year={2022}, publisher={Springer} }

Alvarez, I. (2022). . .

Special Issue

Published in ‘MDPI Sensors’, 2022

Abstract

Connected and automated driving technologies have the potential to revolutionize transportation by facilitating mobility services to a wider population, improving safety and traffic efficiency. Automated driving technology is expected to reduce the number of accidents caused by human error and avert deadly crashes, ensure mobility for all, including old and impaired individuals, allow the human driver to perform alternative (secondary) tasks, increase traffic flow efficiency, reduce fuel consumption, and lower emissions.

Driven by these goals, humankind is experiencing an exponential growth in vehicle automation taking over the monitoring of surroundings and vehicle control tasks from human drivers in a quest towards full autonomy. Connected and automated vehicles are equipped with multimodal sensors that allow continuous perception and monitoring of driving tasks to assist drivers in lower levels of SAE automation or to fully take control of driving tasks under full SAE automation. Numerous sensors, both inside and outside vehicles, allow the detection and identification of oncoming obstacles, the determination of their velocity, and the prediction of future behaviours to avoid potential collisions. Each sensor has its own strengths and weaknesses in terms of range, accuracy, energy consumption. and sensitivity towards external conditions such as weather and light. Automated vehicles usually rely on a mix of signals to improve operational reliability and robustness under the dynamic external conditions of real-world deployments. Generally, we can divide external AV sensors into two major groups: active and passive. Active sensors generate an active signal (electromagnetic or light) transmitted to the external environment to analyse its reflection (e.g., radar, lidar), whereas passive sensors just record the information from the environment (e.g., camera). Additionally, there have been advances in intelligent transportation infrastructure to monitor road users, perform predictive analytics, and facilitate collaborative perception services and remote vehicle control.

The increasing commercial availability of conditional automation (SAE level 3) and the incoming Robotaxi services (SAE Level 4) have also resulted in an increase in in-cabin monitoring sensors dedicated to monitoring driver and passenger behaviours. Multimodal in-cabin monitoring systems are crucial enablers for successfully managing automated vehicle operations. These systems enable the detection of the driver/passenger’s physiological state and activity to assess their readiness to take over control of the vehicle if required as well as to monitor their safety. Driving monitoring solutions provide information on occupants’ fatigue, distraction, discomfort, and stress. Furthermore, they can help to verify that automation is used properly by evaluating engagement in the driving monitoring task or the inherent risk of the non-driving tasks.

This Special issue aims to collect original theoretical or empirical articles on different sensing technologies, solutions, and applications for automated vehicles.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

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Citation

@article{SpecialIssue, author = {Ignacio Alvarez, Jaka Sodnik and Nikolas Thomopoulos}, title = {Robust Multimodal Sensing for Automated Driving Systems}, journal = {Sensors}, year = {2022}, volume = {22}, issue = {13}, pages = {}, doi = {ISSN 1424-8220} }

Ignacio Alvarez, Jaka Sodnik and Nikolas Thomopoulos (2022). Special Issue. Sensors.

Quantifying Error Propagation in Multi-Stage Perception System of Autonomous Vehicles via Physics-Based Simulation

Published in Proceedings of the IEEE 2022 Winter Simulation Conference, IEEE 2023

Abstract

Ensuring the safety of autonomous vehicle (AV) relies on accurate prediction of error occurrences in its perception system. Due to the inter-stage functional dependence, the error occurred at a certain stage may be propagated to the following stage and generate extra errors. To quantify the error propagation, this paper adopts the physics-based simulation, which enables fault injection at different stages of an AV perception system to generate error event data for error propagation modeling. A multi -stage Hawkes process (MSHP) is proposed to predict the error occurrences in each stage, with error propagation represented as a latent triggering mechanism. With explicitly considering the error propagation mechanism, the proposed outperforms benchmark methods in predicting error occurrence in a physics-based simulation of a multistage AV perception system. The proposed two-step likelihood-based algorithm accurately estimates the model coefficients in a numerical simulation case study.

Key Contributions

Integrated simulation and modeling framework
Fault-injection simulation for data generation
Statistical error propagation modeling
Model parameter estimation algorithm
Model validation and performance benchmarking

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Citation

@inproceedings{pan2022quantifying, title={Quantifying error propagation in multi-stage perception system of autonomous vehicles via physics-based simulation}, author={Pan, Fenglian and Zhang, Yinwei and Head, Larry and Liu, Jian and Elli, Maria and Alvarez, Ignacio}, booktitle={2022 Winter Simulation Conference (WSC)}, pages={2511--2522}, year={2022}, organization={IEEE} }

Alvarez, I. (2022). . 2022 Winter Simulation Conference (WSC).

IEEE Standard for Assumptions in Safety-Related Models for Automated Driving Systems

Abstract

This standard applies to road vehicles. It defines a minimum set of reasonable assumptions and foreseeable scenarios that shall be considered in the development of safety related models that are part of an automated driving system (ADS). Scope: This standard applies to road vehicles. For a set of scenarios, a minimum set of assumptions regarding reasonably foreseeable behaviors of other road users are defined that shall be considered in the development of safety-related models for automated driving systems (ADS). This standard further defines a list of attributes common to contributed safety-related models and methods to help verify whether a safety-related model takes the minimum set of assumptions into consideration. An informative annex instantiates several examples of how the proposed minimum set of assumptions could be employed in ADS development. Sources of uncertainty, such as prediction or perception errors, are out of scope to this standard. This standard does not guarantee the safety of the overall system in all scenarios. Purpose: Government and Industry alike need an open, transparent, and technology-neutral standard that provides guidance useful for evaluating the performance of an ADS. This guidance consists of a minimum set of assumptions with bounds on reasonably foreseeable behaviors of other road users used in the development of safety-related models.

Key Contributions

Minimum assumption definition for ADAS
Reasonable Foreseeable scenario specification
Technology-neutral evaluation guidance
Model verification methodology

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BibTeX

@ARTICLE{9761121, author={}, journal={IEEE Std 2846-2022}, title={IEEE Standard for Assumptions in Safety-Related Models for Automated Driving Systems}, year={2022}, volume={}, number={}, pages={1-59}, keywords={IEEE Standards;Autonomous vehicles;Vehicle safety;Autonomous driving;Vehicle driving;Data privacy;Decision making;ADS;ADS-operated vehicle;automated driving system;assumption;automated vehicle;autonomous vehicles;AV;decision-making;IEEE 2846}, doi={10.1109/IEEESTD.2022.9761121}}

Empathic vehicle design: Use cases and design directions from two workshops

Published in ‘Proceedings of ACM’s Computer Human Interaction Conference (CHI)’, ACM 2022

Abstract

Empathic vehicles are expected to improve user experience in automated vehicles and to help increase user acceptance of technology. However, little is known about potential real-world implementations and designs using empathic interfaces in vehicles with higher levels of automation. Given advances in affect detection and emotion mitigation, we conducted two workshops (N1 =24, N2 = 22, Ntotal = 46) on the design of empathic vehicles and their potential utility in a variety of applications. This paper recapitulates key opportunities in the design and application of empathetic interfaces in automated vehicles which emerged from the two workshops hosted at the ACM AutoUI conferences.

Key Contributions

Emphathetic HMI design in Automotive
Generative AI prototyping
Workshop organization and community building

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Citation

@inproceedings{nadri2022empathic, title={Empathic vehicle design: Use cases and design directions from two workshops}, author={Nadri, Chihab and Alvarez, Ignacio and Bosch, Esther and Oehl, Michael and Braun, Michael and Healey, Jennifer and Jallais, Christophe and Ju, Wendy and Li, Jingyi and Jeon, Myounghoon}, booktitle={CHI Conference on Human Factors in Computing Systems Extended Abstracts}, pages={1--7}, year={2022} }

Nadri, Chihab and Alvarez, Ignacio and Bosch, Esther and Oehl, Michael and Braun, Michael and Healey, Jennifer and Jallais, Christophe and Ju, Wendy and Li, Jingyi and Jeon, Myounghoon (2022). Empathic vehicle design: Use cases and design directions from two workshops. CHI Conference on Human Factors in Computing Systems Extended Abstracts.

Emotion GaRage Vol. III: A Workshop on Affective In-Vehicle Display Applications

Published in ‘Adjunct Proceedings of the 14th International Conference on Automotive User Interfaces and Interactive Vehicular Applications’, ACM 2022

Abstract

Empathic in-vehicle interfaces can address driver affect and mitigate decreases in driving performance and behavior that are associated with emotional states. Empathic vehicles can detect and employ a variety of intervention modalities to change user affect and improve user experience. Challenges remain in the implementation of such strategies, as a broader established view of practical intervention modalities and strategies is still absent. Therefore, we propose a workshop that aims to bring together researchers and practitioners interested in affective interfaces and in-vehicle technologies as a forum for the development of displays and alternatives suitable to various use case situations in current and future vehicle states. During the workshop, we will focus on a common set of use cases and generate approaches that can suit different user groups. By the end of this workshop, researchers will create a design flowchart for in-vehicle affective display designers when creating displays for an empathic vehicle.

Key Contributions

HCI Interaction Design
Emotional and affective computing
HCI Prototyping
Workshop organization and community building

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Citation

@inproceedings{10.1145/3544999.3550161, author = {Nadri, Chihab and Dong, Jiayuan and Li, Jingyi and Alvarez, Ignacio and Jeon, Myounghoon}, title = {Emotion GaRage Vol. III: A Workshop on Affective In-Vehicle Display Applications}, year = {2022}, isbn = {9781450394284}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3544999.3550161}, doi = {10.1145/3544999.3550161}, pages = {186–188}, numpages = {3}, keywords = {Empathic vehicles, affective computing, emotions, human-centered computing, interaction design, user experience}, location = {Seoul, Republic of Korea}, series = {AutomotiveUI '22} }

Nadri, Chihab and Dong, Jiayuan and Li, Jingyi and Alvarez, Ignacio and Jeon, Myounghoon (2022). Emotion GaRage Vol. III: A Workshop on Affective In-Vehicle Display Applications. .

Driver emotions in automated vehicles

Abstract

There is little doubt that driving generates emotional responses, whether that’s the joy of freedom, the boredom of stop-and-go traffic or anger towards unsafe maneuvers. In this chapter we provide an overview of emotion research applied to the automotive context and highlight the impact of emotional states in varying levels of driving automation. We review the most critical research findings on the impact of emotional states in driving performance including reaction time and take-over readiness. We also discuss the application of emotion regulation strategies related to the driving task. Finally, we analyze the research challenges still present for robust emotional classification and personalization in their application to in-vehicle interactions. This technology offers great potential for the development of emotionally-aware in-cabin driver assistants which will play a critical role in the future of automated driving user experience development.

Key Contributions

Foundational theory compilation
UX design methods and frameworks
Interface application and case studies
Academia-industry knowledge transfer
Holistic user-centered design advocacy

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Citation

@incollection{fakhrhosseini2022driver, title={Driver emotions in automated vehicles}, author={FakhrHosseini, Shabnam and Ko, Sangjin and Alvarez, Ignacio and Jeon, Myounghoon}, booktitle={User Experience Design in the Era of Automated Driving}, pages={85--97}, year={2022}, publisher={Springer} }

Alvarez, I. (2022). . User Experience Design in the Era of Automated Driving.

2021

To err is human: The role of human derived safety metrics in an age of automated vehicles

Published in ‘SAE Technical Paper’, SAE 2021

Abstract

As industry races to complete technical development of automated driving systems (ADS), important questions are being raised about how to measure the safety of such systems and the overall safety of Automated Vehicles (AVs). Traffic safety engineers have for decades utilized metrics to assess the safety of human drivers and measurements such as Time To Collision (TTC) and Time Headway (THW) have proven to be a useful indicator of increased risk of an accident for human drivers. But what if we can do better with AVs? Are human driving derived risk metrics meaningful for a self-driving vehicle? Recently, the Institute for Automated Mobility (IAM) published a set of metrics defined specifically for self-driving vehicles that provide a thorough assessment of the safety of an AV. While humans must use estimation and cautious judgement to make decisions, AVs can use precise measurement techniques via sensors and correlate multiple sources of data in real time. Utilizing information such as the reaction time of the ADS, the braking capability of the AV and more, the IAM proposed metrics allow for the assessment of the safety of an AV to be accurately measured, not as a notion of approximated risk, but as a binary calculation of safety. In this paper we analyze, compare and contrast human driving, risk-oriented safety metrics with the more definitive metrics proposed for AVs. We answer important questions about the necessary evolution of human derived metrics to ensure they are meaningful in the assessment of the safety of an AV, as well as whether novel metrics proposed for AVs can be used to better understand and assess the safety performance of AVs when compared to historical safety measures. Our research proves that AV-based assessment metrics can provide better insight into the safety of both AVs and human drivers.

Key Contributions

Safety analysis and validation
Automated driving system development

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Citation

@techreport{weast2021err, title={To err is human: The role of human derived safety metrics in an age of automated vehicles}, author={Weast, Jack and Elli, Maria and Alvarez, Ignacio}, year={2021}, institution={SAE Technical Paper} }

Weast, Jack and Elli, Maria and Alvarez, Ignacio (2021). To err is human: The role of human derived safety metrics in an age of automated vehicles. .

On responsibility sensitive safety in car-following situations-a parameter analysis on german highways

Published in Proceedings of the IEEE Intelligent Vehicles Symposium (IV), IEEE 2021

Abstract

The need for safety in automated driving is undisputed. Since automated vehicles are expected to reduce the number of fatalities in road traffic significantly, hundreds of millions of test kilometers would be required for statistical safety validation [1]. Physics-based safety verification approaches are promising in order to reduce this validation effort. Towards this goal, Mobileye introduced the concept of Responsibility-Sensitive Safety (RSS). In RSS, bounds for the reasonable worst-case behavior of traffic participants are assumed to be given, such as the reaction time or the maximum deceleration. These parameters have a crucial effect on the applicability of the approach: choosing conservative parameters likely hinders traffic flow, while the opposite could lead to collisions, as the assumptions are violated. Thus, in this work, we focus on finding reasonable parameters of RSS. Based on the physical limits, legal requirements and human driving behavior, we propose scopes and parameter sets that allow for a sound safety verification while not hindering traffic flow. Furthermore, we present an approach that explains seemingly frequent human drivers’ RSS violations on highways and may lead to a useful extension of RSS.

Key Contributions

Safety analysis and validation
Automated driving system development
Verification and validation methods
Parameter Analysis

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@INPROCEEDINGS{9575420, author={Naumann, Maximilian and Wirth, Florian and Oboril, Fabian and Scholl, Kay–Ulrich and Elli, Maria Soledad and Alvarez, Ignacio and Weast, Jack and Stiller, Christoph}, booktitle={2021 IEEE Intelligent Vehicles Symposium (IV)}, title={On Responsibility Sensitive Safety in Car-following Situations - A Parameter Analysis on German Highways}, year={2021}, volume={}, number={}, pages={83-90}, keywords={Law;Intelligent vehicles;Roads;Safety;Brakes;Responsibility-Sensitive Safety (RSS);Automated Driving (AD);Safety Verification;Parameter Analysis}, doi={10.1109/IV48863.2021.9575420}}

Alvarez, I. (2021). . 2021 IEEE Intelligent Vehicles Symposium (IV).

MISO-V: Misbehavior detection for collective perception services in vehicular communications

Published in ‘Proceedings of IEEE Intelligent Transportation Systems Conference 2021’, IEEE 2021

Abstract

Recently, Collective Perception Messages (CPM) that carry additional information about the surrounding environment beyond Basic Safety Messages (BSM) or Cooperative Awareness Messages (CAM) have been proposed to increase the situational awareness for Connected and Automated Vehicles (CAV) in Intelligent Transportation Systems. However, blindly trusting perception information from neighbors that cannot be locally verified is dangerous given the safety impact that erroneous or malicious information might have. This paper addresses the data trust challenge of CPMs, proposing a misbehavior detection scheme called MISO- V (Multiple Independent Sources of Observations over V2X) that leverages the inherently overlapping nature of the perception observations from multiple vehicles to verify the semantic correctness of the V2X data and improve the data trust and robustness of V2X systems. CPM-enabled CAVs are implemented and MISO-V performance is evaluated in CARLA-based simulation tool, where falsified V2X packets presenting a ghost car are injected in a suburban T-junction scenario with other cars. The results show that MISO- V is very effective in detecting the ghost car attacks and removing the impact of such misbehavior from influencing the receiver and offers a conservative and sensible approach towards trustworthy Collective Perception Services for CAVs.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

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Citation

@INPROCEEDINGS{9575970, author={Liu, Xiruo and Yang, Lily and Alvarez, Ignacio and Sivanesan, Kathiravetpillai and Merwaday, Arvind and Oboril, Fabian and Buerkle, Cornelius and Sastry, Manoj and Baltar, Leonardo Gomes}, booktitle={2021 IEEE Intelligent Vehicles Symposium (IV)}, title={MISO- V: Misbehavior Detection for Collective Perception Services in Vehicular Communications}, year={2021}, volume={}, number={}, pages={369-376}, keywords={Simulation;Semantics;Redundancy;Receivers;Tools;Robustness;Safety}, doi={10.1109/IV48863.2021.9575970} }

Liu, Xiruo and Yang, Lily and Alvarez, Ignacio and Sivanesan, Kathiravetpillai and Merwaday, Arvind and Oboril, Fabian and Buerkle, Cornelius and Sastry, Manoj and Baltar, Leonardo Gomes (2021). MISO-V: Misbehavior detection for collective perception services in vehicular communications. 2021 IEEE Intelligent Vehicles Symposium (IV).

2020

PURSS: Towards Perceptual Uncertainty Aware Responsibility Sensitive Safety with ML.

Published in ‘Proceedings of 2020 AAAI Conference’, AAAI 2020

Abstract

Automated driving is an ML-intensive problem and its safety depends on the integrity of perception as well as planning and control. Responsibility Sensitive Safety (RSS) is a recent approach to promote safe planning and control that relies on perfect perception; however, perceptual uncertainty is always present, and this causes the possibility of misperceptions that can lead an autonomous vehicle to allow unsafe actions. In this position paper, we sketch a novel proposal for a formal model of perception coupled with RSS to help mitigate the impact of misperception by using information about perceptual uncertainty. The approach expresses uncertainty as imprecise perceptions that are consumed by RSS and cause it to limit actions to those that support safe behaviour given the perceptual uncertainty. We illustrate our approach using examples and discuss its implications and limitations.

Key Contributions

Safety analysis and validation

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@inproceedings{salay2020purss, title={PURSS: Towards Perceptual Uncertainty Aware Responsibility Sensitive Safety with ML.}, author={Salay, Rick and Czarnecki, Krzysztof and Elli, Maria Soledad and Alvarez, Ignacio J and Sedwards, Sean and Weast, Jack}, booktitle={SafeAI@ AAAI}, pages={91--95}, year={2020}, organization={New York} }

Salay, Rick and Czarnecki, Krzysztof and Elli, Maria Soledad and Alvarez, Ignacio J and Sedwards, Sean and Weast, Jack (2020). PURSS: Towards Perceptual Uncertainty Aware Responsibility Sensitive Safety with ML.. SafeAI@ AAAI.

How safe is safe enough? Automatic safety constraints boundary estimation for decision-making in automated vehicles

Published in Proceedings of IEEE Intelligent Vehicle Symposium 2020, IEEE 2020.

Abstract

The determination of safety assurances for automated driving vehicles is one of the most critical challenges in the industry today. Several behavioral safety models for automated driving have been proposed recently and standards discussions are on the way. In this paper we present a method to automatically explore the performance of automated vehicle (AV) safety models utilizing robustness of Metric Temporal Logic (MTL) specifications as a continuous metric of safety. We present a case study of the Responsibility Sensitive Safety model (RSS), introducing a safety evaluation pipeline based on the CARLA driving simulator, RSS and a set of safety-critical driving scenarios. Our method automatically extracts safety relevant profiles for these scenarios providing practical parametric boundaries for implementation. Furthermore, we evaluate the trade-offs between safety and utility within the safe RSS parameter space through a proposed naturalistic benchmark challenge that we open-sourced. We analyze different RSS parameter configurations including assertive and more conservative settings, extracted by our specification-driven framework. Our results show that while maintaining the safety boundaries, the extracted RSS configuration for assertive driving behavior achieves the highest utility.

Key Contributions

Safety boundary estimation framework
Parametric and non-parametric estimation methods
Simulation-based data generation and validation
Real-world data application and analysis
Systematic safety parameter definition

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@inproceedings{rodionova2020safe, title={How safe is safe enough? Automatic safety constraints boundary estimation for decision-making in automated vehicles}, author={Rodionova, Al{"e}na and Alvarez, Ignacio and Elli, Maria Soledad and Oboril, Fabian and Quast, Johannes and Mangharam, Rahul}, booktitle={2020 IEEE Intelligent Vehicles Symposium (IV)}, pages={1457--1464}, year={2020}, organization={IEEE} }

Alvarez, I. (2020). . 2020 IEEE Intelligent Vehicles Symposium (IV).

Evaluation of responsibility-sensitive safety (rss) model based on human-in-the-loop driving simulation

Published in In proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, IEEE 2020.

Abstract

Safety is an important challenge in the development of automated vehicles (AVs). To help with the challenge of achieving higher safety in the decision making of AVs, Intel and Mobileye have proposed a parameterized model named Responsibility-Sensitive Safety (RSS). Previous studies have demonstrated that RSS has the potential to improve the safety performance of automated vehicles. However, RSS could lead to a considerable car-following distance depending on the parameter values chosen for the model, which could reduce traffic efficiency. To improve the efficiency of RSS applied to Adaptive Cruise Control (ACC) systems, previous work proposed an efficiency-optimal (referred as “Efficiency-optimal RSS”) variation of the RSS model that involves different triggering conditions of a proper response. Therefore, in this paper a human-in-the-loop driving simulation experiment was conducted to evaluate the performance and acceptance of different safety methods. The RSS model and the efficiencyoptimal variant were embedded in an ACC system based on Model Predictive Control (MPC) algorithm. Two car-following scenarios with a sudden deceleration of lead vehicle at various time headways were simulated to evaluate the human perception and response of the different models. Results show that the original RSS model improves subjective safety judgment of human drivers. While the Efficiency-optimal RSS variant has a lower subjective safety score when compared to original RSS, it significantly reduces driver’s emergency braking reactions when compare to an ACC only system.

Key Contributions

Human-in-the-loop evaluation framework
Comparative performance analysis (RSS vs. Human)
Safety model parameter sensitivity analysis
Empirical validation of a formal safety model
Simulation-based model calibration methodology

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@inproceedings{chai2020evaluation, title={Evaluation of responsibility-sensitive safety (rss) model based on human-in-the-loop driving simulation}, author={Chai, Chen and Zeng, Xianming and Alvarez, Ignacio and Elli, Maria Soledad}, booktitle={2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)}, pages={1--7}, year={2020}, organization={IEEE} }

Alvarez, I. (2020). . 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC).

Emotion GaRage Vol. II A Workshop on Affective In-Vehicle Display Design

Published in ‘Proceedings of 12th International Conference in Automotive User Interfaces and Interactive Vehicular Applications’, 2020

Abstract

Driver performance and behavior can be partially predicated based on one’s emotional state. Through ascertaining the emotional state of passengers and employing various mitigation strategies, empathic cars can show potential in improving user experience and driving performance. Challenges remain in the implementation of such strategies, as individual differences play a large role in mediating the effect of affective intervention. Therefore, we propose a workshop that aims to bring together researchers and practitioners interested in affective interfaces and in-vehicle technologies as a forum for the development of targeted emotion intervention methods. During the workshop, we will focus on a common set of use cases and generate approaches that can suit different user groups. By the end of this short workshop, researchers will determine ideal intervention methods for prospective user groups. This will be achieved through the method of insight combination to generate and discuss ideas.

Key Contributions

Affective Computing in Automotive
Emotion-Aware HMI Design
Community Building

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@misc{nadri2020emotion, title={Emotion GaRage Vol. II}, author={Nadri, Chihab and Bosch, E and Oehl, M and Alvarez, I and Braun, M and Jeon, M}, year={2020}, publisher={II} }

Nadri, Chihab and Bosch, E and Oehl, M and Alvarez, I and Braun, M and Jeon, M (2020). Emotion GaRage Vol. II A Workshop on Affective In-Vehicle Display Design. .

Auto-UI Global Perspectives

Published in ACM IX Interaction Journal, 2020

Abstract

ACM SIGCHI Auto-UI is a growing community, but one in which some continents were less involved than expected and hoped for. For the 2019 conference in Utrecht, the Netherlands, we made various targeted attempts to grow and diversify our international community, with support from the ACM SIGCHI Development Fund. Our efforts resulted in a growth in the number of Asian participants, which made up almost 20 percent of the attendees. In this blog, we briefly reflect on our initiatives and on a panel discussion focusing on research topics that matter more globally to the Auto-UI field.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

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@article{janssen2020auto, title={Auto-UI: global perspectives}, author={Janssen, Christian P and Schroeter, Ronald and Bidwell, Nicola J and Ji, Yong Gu and Alvarez, Ignacio and Bao, Shan and Jeon, Myounghoon and Boyle, Linda Ng and Donker, Stella F and Chuang, Lewis L and others}, journal={Interactions}, volume={27}, number={6}, pages={7--9}, year={2020}, publisher={ACM New York, NY, USA} }

Janssen, Christian P and Schroeter, Ronald and Bidwell, Nicola J and Ji, Yong Gu and Alvarez, Ignacio and Bao, Shan and Jeon, Myounghoon and Boyle, Linda Ng and Donker, Stella F and Chuang, Lewis L and others (2020). Auto-UI Global Perspectives. Interactions.

Agents, environments, scenarios: A framework for examining models and simulations of human-vehicle interaction

Published in ‘Transportation research interdisciplinary perspectives’, 2020

Abstract

This paper provides a framework for examining human-vehicle interactions with respect to three dimensions that can involve models or simulations: the agents, the environments, and the scenarios. Agents are considered on a spectrum from human to artificial actors. Environments are considered on a spectrum from simulated to real. Scenarios are considered on a spectrum from constrained to unconstrained. It is argued that these three dimensions capture key differences in research approaches within the field of human-vehicle interaction, and that explicitly situating research and discussions within this framework will allow researchers to better compare and contrast research outcomes and contributions. The framework is used to locate different disciplines in the community with respect to one another, and to identify areas which are as-yet unexplored.

Key Contributions

Human-computer interaction design
Dimensions of simulation models
Automated Driving Development

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Citation

@article{janssen2020agents, title={Agents, environments, scenarios: A framework for examining models and simulations of human-vehicle interaction}, author={Janssen, Christian P and Boyle, Linda Ng and Ju, Wendy and Riener, Andreas and Alvarez, Ignacio}, journal={Transportation research interdisciplinary perspectives}, volume={8}, pages={100214}, year={2020}, publisher={Elsevier} }

Janssen, Christian P and Boyle, Linda Ng and Ju, Wendy and Riener, Andreas and Alvarez, Ignacio (2020). Agents, environments, scenarios: A framework for examining models and simulations of human-vehicle interaction. Transportation research interdisciplinary perspectives.

2019

Towards standardization of AV safety: C++ library for responsibility sensitive safety

Published in ‘Proceedings of the IEEE Intelligent Vehicle Symposium 2019’, IEEE 2019

Abstract

The need for safety in Automated Driving (AD) is becoming increasingly critical with the accelerating deployment of this technology. Beyond functional safety, industry must guarantee the operational safety of automated vehicles. Towards that end, Mobileye introduced the Responsibility Sensitive Safety (RSS), a model-based approach to Safety [1]. In this paper we expand upon this work introducing the C++ Library for Responsibility Sensitive Safety, an open source executable that implements a subset of RSS. We provide architectural details to integrate the C++ Library for Responsibility Sensitive Safety with AD Software pipelines as safety module overseeing decision making of driving policies. We illustrate this application with an example integration with the Baidu Apollo AD stack and simulator, [2] and [3], that provides safety validation of the planning module. Furthermore, we show how the C++ Library for Responsibility Sensitive Safety can be used to explore the usefulness of the RSS model through parameter exploration and analysis on minimum safe longitudinal distance, (dmin), considering different weather conditions. We also compare these results with half-of-speed rule followed in some parts of the world. We expect that the C++ Library for Responsibility Sensitive Safety becomes a critical component of future tools for formal verification, testing and validation of AD safety and that it helps bootstrap the AD research efforts towards standardization of safety.

Key Contributions

Safety analysis and validation
Standardized C++ library implementation
Modular software architecture design
Verification and validation methodology
Open-source safety model deployment

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Citation

@inproceedings{gassmann2019towards, title={Towards standardization of AV safety: C++ library for responsibility sensitive safety}, author={Gassmann, Bernd and Oboril, Fabian and Buerkle, Cornelius and Liu, Shuang and Yan, Shoumeng and Elli, Maria Soledad and Alvarez, Ignacio and Aerrabotu, Naveen and Jaber, Suhel and Van Beek, Peter and others}, booktitle={2019 IEEE Intelligent Vehicles Symposium (IV)}, pages={2265--2271}, year={2019}, organization={IEEE} }

Gassmann, Bernd and Oboril, Fabian and Buerkle, Cornelius and Liu, Shuang and Yan, Shoumeng and Elli, Maria Soledad and Alvarez, Ignacio and Aerrabotu, Naveen and Jaber, Suhel and Van Beek, Peter and others (2019). Towards standardization of AV safety: C++ library for responsibility sensitive safety. 2019 IEEE Intelligent Vehicles Symposium (IV).

The SKYNIVI experience: evoking startle and frustration in dyads and single drivers

Published in ‘Proceedings of the IEEE International Conference in Intelligent Transportations Systems 2019’, IEEE 2019

Abstract

To study naturalistic in-cabin emotion we developed SKYNIVI, a modified open source driving simulator, with scenarios designed to elicit startle and frustration. We target generating these emotions because we believe that by detecting these it will be possible for autonomous vehicles to learn to drive better. We show how to use SKYNIVI to develop datasets that capture naturalistic emotions in drivers and passengers for algorithmic development. We recruited 51 participants as dyads and single drivers to participate in two different scenarios. We show that we were able to evoke hundreds of instances of our target emotions in this cohort and present an analysis of factors we found to impact emotional expression including: scenario design , demographic factors, personality and baseline affect . We find that having a second person in the vehicle impacts observed expressions of emotion even when no difference in baseline affect is reported.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

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Citation

@INPROCEEDINGS{8813831, author={Alvarez, Ignacio and Healey, Jennifer and Lewis, Erica}, booktitle={2019 IEEE Intelligent Vehicles Symposium (IV)}, title={The SKYNIVI Experience: Evoking Startle and Frustration in Dyads and Single Drivers}, year={2019}, volume={}, number={}, pages={76-81}, keywords={Automobiles;Autonomous vehicles;Monitoring;Roads;Training}, doi={10.1109/IVS.2019.8813831} }

Alvarez, Ignacio and Healey, Jennifer and Lewis, Erica (2019). The SKYNIVI experience: evoking startle and frustration in dyads and single drivers. 2019 IEEE Intelligent Vehicles Symposium (IV).

Object-level perception sharing among connected vehicles

Published in Proceedings of 2019 IEEE Intelligent Transportation Systems Conference (ITSC), IEEE 2023

Abstract

Advances in vehicular communication technologies have made Connected Vehicles (CVs) a near-term reality. Compared to models where vehicles rely solely on their own perception to sense and make decision on the environment, CVs have the potential to allow information sharing among vehicles to improve sensing and decision making collectively. A first concrete step towards this goal is enabling vehicles to share perception-related information, to overcome limitations of their respective sensors (e.g., partial awareness due to occlusions). Sharing processed information vs raw sensor data has the advantage of reducing the amount of data to be transmitted, and the required computation burden at the receiver side.This paper proposes an approach to enable object-level sharing among vehicles. Following state-of-the-art object-level management techniques, we developed a two-layer architecture that handles object tracking and fusion from dynamic remote sources of information. We implemented our approach and showed that it can achieve realistic performance, and robustness both in terms of quality of information and computation.

Key Contributions

Proposes two-layer object fusion architecture
Defines connected vehicles as virtual sensors
Dynamically manages vehicles entering/leaving range
uses objects using distance-based clustering
Evaluates system accuracy and scalability

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Citation

@INPROCEEDINGS{8916837, author={Ambrosin, Moreno and Alvarez, Ignacio J and Buerkle, Cornelius and Yang, Lily L and Oboril, Fabian and Sastry, Manoj R and Sivanesan, Kathiravetpillai}, booktitle={2019 IEEE Intelligent Transportation Systems Conference (ITSC)}, title={Object-level Perception Sharing Among Connected Vehicles}, year={2019}, volume={}, number={}, pages={1566-1573}, keywords={Sensor fusion;Vehicle dynamics;Standards;Collaboration;Merging;Covariance matrices}, doi={10.1109/ITSC.2019.8916837} }

Alvarez, I. (2019). . 2019 IEEE Intelligent Transportation Systems Conference (ITSC).

Human–vehicle cooperation in automated driving: A multidisciplinary review and appraisal

Published in ‘International Journal of Human Computer Interaction’, Elsevier 2019

Abstract

To draw a comprehensive and cohesive understanding of human–vehicle cooperation in automated driving, a review is made on key studies in human–robot interaction and human factors. Throughout this article, insight is provided into how human drivers and vehicle systems interplay and influence each other. The limitations of technology-centered taxonomies of automation are discussed and the benefits of accounting for human agents are examined. The contributions of machine learning to automated driving and how critical models in human-system cooperation can inform the design of a more symbiotic relationship between driver and vehicle are investigated. Challenges in the human element to enable the safe introduction of road automation are also discussed. Particularly, the unintended consequences of vehicle automation on driver’s workload, situation awareness and trust are examined, and the social interactions between driver, vehicle, and other road users are investigated. This review will help professionals shape future directions for safer and more efficient and effective human–vehicle cooperation.

Key Contributions

Critiques technology-centered automation taxonomies
Promotes human-centered automation design.
Analyzes automation’s impact on drivers
Reviews machine learning for cooperation
Examines social human-vehicle interactions

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Citation

@article{biondi2019human, title={Human--vehicle cooperation in automated driving: A multidisciplinary review and appraisal}, author={Biondi, Francesco and Alvarez, Ignacio and Jeong, Kyeong-Ah}, journal={International Journal of Human--Computer Interaction}, volume={35}, number={11}, pages={932--946}, year={2019}, publisher={Taylor \& Francis} }

Biondi, Francesco and Alvarez, Ignacio and Jeong, Kyeong-Ah (2019). Human–vehicle cooperation in automated driving: A multidisciplinary review and appraisal. International Journal of Human--Computer Interaction.

Human-system cooperation in automated driving

Published in ‘International Journal of Human Computer Interaction’, Elsevier 2019

Abstract

To draw a comprehensive and cohesive understanding of human–vehicle cooperation in automated driving, a review is made on key studies in human–robot interaction and human factors. Throughout this article, insight is provided into how human drivers and vehicle systems interplay and influence each other. The limitations of technology-centered taxonomies of automation are discussed and the benefits of accounting for human agents are examined. The contributions of machine learning to automated driving and how critical models in human-system cooperation can inform the design of a more symbiotic relationship between driver and vehicle are investigated. Challenges in the human element to enable the safe introduction of road automation are also discussed. Particularly, the unintended consequences of vehicle automation on driver’s workload, situation awareness and trust are examined, and the social interactions between driver, vehicle, and other road users are investigated. This review will help professionals shape future directions for safer and more efficient and effective human–vehicle cooperation.

Key Contributions

Critiques technology-centered automation taxonomies
Promotes human-centered automation design.
Analyzes automation’s impact on drivers
Reviews machine learning for cooperation
Examines social human-vehicle interactions

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Citation

@article{biondi2019human, title={Human--vehicle cooperation in automated driving: A multidisciplinary review and appraisal}, author={Biondi, Francesco and Alvarez, Ignacio and Jeong, Kyeong-Ah}, journal={International Journal of Human--Computer Interaction}, volume={35}, number={11}, pages={932--946}, year={2019}, publisher={Taylor \& Francis} }

Biondi, Francesco and Alvarez, Ignacio and Jeong, Kyeong-Ah (2019). Human-system cooperation in automated driving. International Journal of Human--Computer Interaction.

Design of a misbehavior detection system for objects based shared perception V2X applications

Published in ‘IEEE Intelligent Transportation Systems Conference’, IEEE 2019

Abstract

The recent trend of integrating vehicular communications with advanced sensors installed on vehicles, enables Connected and Autonomous Vehicles (CAVs) to share their own driving information as well as perception information, such as a list of perceived objects (e.g., dynamic obstacles such as vehicles, pedestrians, and cyclists, and static obstacles). This has the potential to improve driving safety by expanding collective perception of vehicles. However, adversaries may also populate false information to other Connected Vehicles (CVs) via Vehicle-to-Vehicle (V2V) communications. This paper investigates the security aspects of mixed deployment of CAVs, CVs and legacy vehicles, and in particular with regards to misbehavior detection. We provide a generic design framework that is independent from the specific algorithms of the underlying perception system, and can be used to implement a practical Misbehavior Detection System (MDS). We analyze the MDS framework w.r.t. a ghost vehicle attack. While no computing system can be completely secure, we believe this work would help the industry to develop a practical MDS design within a common framework while allowing individual techniques to mature and evolve over time with future academic research.

Key Contributions

Proposes a generic misbehavior detection framework
Divides detection into local/collaborative components
Categorizes four levels of anomaly detection
Defines Unobservability and Undecidability concepts
Defines conditions for successful attacks

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Citation

@inproceedings{8917066, author={Ambrosin, Moreno and Yang, Lily L and Liu, Xiruo and Sastry, Manoj R and Alvarez, Ignacio J}, booktitle={2019 IEEE Intelligent Transportation Systems Conference (ITSC)}, title={Design of a Misbehavior Detection System for Objects Based Shared Perception V2X Applications}, year={2019}, volume={}, number={}, pages={1165-1172}, keywords={Sensors;Autonomous vehicles;Roads;Safety;Space vehicles;Time measurement;Security}, doi={10.1109/ITSC.2019.8917066} }

Ambrosin, Moreno and Yang, Lily L and Liu, Xiruo and Sastry, Manoj R and Alvarez, Ignacio J (2019). Design of a misbehavior detection system for objects based shared perception V2X applications. 2019 IEEE Intelligent Transportation Systems Conference (ITSC).

2nd special session on solving the automated vehicle safety assurance challenge

Published in Proceedings of the 2019 International Conference on Intelligent Transportation Systems (ITSC), IEEE 2019

Abstract

The scientific knowledge and tools for the mass deployment of Automated Vehicles (AVs) are maturing rapidly, as evidenced by the wide deployment of AV test fleets worldwide. These vehicles have the potential to produce tremendous economic and societal benefits including greatly reduced traffic accidents, injuries, and congestion, and to make less expensive, more flexible and more productive transportation available to all. But one challenge looms above all others in the race to full vehicle automation; solving the AV Safety Assurance challenge. Automated transportation is not just a product, but an industry. And it is as an industry that we must together solve this challenge. We invite researchers, automakers, technology companies, and government regulators to come together to develop a holistic model to define and measure AV Safety. In this special session we present papers that provide contributions to the definition, applicability and standardization of AV safety assurance, including methods for the development of metrics, benchmarks and evangelisation of AV Safety Assurances to users and the public. This session continues the open discussion started in 2018 ITSC with the goal of making AV Safety a reality. The session will consist of high quality paper presentations as well as a panel discussion with some of the most relevant figures of the AV industry.

Key Contributions

Common AV safety definition
Fosters collaboration between industry and academia
Seeks to specify AV safety standards
Gathers a universal view on safety

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Citation

@inproceedings{elli20192nd, title={2nd special session on solving the automated vehicle safety assurance challenge}, author={Elli, Maria Soledad and Alvarez, Ignacio and Ota, Jeffrey and Weast, Jack and Maurer, Markus}, booktitle={International Conference on Intelligent Transportation Systems (ITSC)}, year={2019} }

Alvarez, I. (2019). . International Conference on Intelligent Transportation Systems (ITSC).

2018

Workshop on designing highly automated driving systems as radical innovation

Published in Adjunct Proceedings of the 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’18), ACM 2018

Abstract

Automated driving systems (ADS), especially in higher levels of automation, seem to be the new focus of innovation regarding future mobility. Technological achievements of traveling automation open up new challenges for road traffic. Existing automotive research focuses on problem solving and observational approaches including users and their imagination of the future of mobility to analyze acceptance and user experience of “incremental” (step-wised improved) innovations. On the other hand, “radical” (something new, enabled by technology or meaning change) innovations extensively increase product quality leaping over incremental innovation. This workshop aims to challenge the current research approaches to automated driving against “trying to improve sitting in a horse carriage” and discuss how we can design “radical” innovations for ADS beyond the “horse carriage”. Within this interactive workshop, we will utilize a design thinking approach to refocus on underlying problems that ADSs originally aim to solve and generate ideas for radical innovations.

Key Contributions

Critiques incremental innovation in ADS research
Promotes designing ADS as radical innovation
Applies a design thinking approach
Utilizes rapid prototyping for radical ideas
Refocuses on problems ADS should solve

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Citation

@inproceedings{10.1145/3239092.3239097, author = {Frison, Anna-Katharina and Riener, Andreas and Jeon, Myounghoon and Pfleging, Bastian and Alvarez, Ignacio}, title = {Workshop on Designing Highly Automated Driving Systems as Radical Innovation}, year = {2018}, isbn = {9781450359474}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3239092.3239097}, doi = {10.1145/3239092.3239097}, booktitle = {Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages = {37–43}, numpages = {7}, keywords = {Automated Driving Systems, Design Thinking, Innovation, SAE J3016, User-Centered Design}, location = {Toronto, ON, Canada}, series = {AutomotiveUI '18} }

Alvarez, I. (2018). . Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

Towards understanding emotional reactions of driver-passenger dyads in automated driving

Published in Proceedings of the 13th IEEE International Conference on Automatic Face & Gesture Recognition, IEEE 2018

Abstract

Automated driving has the potential to reduce the amount of fatal crashes, lighten the burden of commutes, and democratize mobility access to wider populations. But delegation of control to automation is not without issues. One of the foreseen drawbacks is that users might experience negative emotional reactions to unanticipated or unexplainable automated maneuvers. In this paper we present a novel method to induce targeted emotional reactions, frustration and startle, in simulated automated driving environments. We describe the data collection process for 17 driver - passenger dyads and discuss the data labelling method for generating reliable novel emotion datasets. This contribution is a foundational methodology towards expanding emotional understanding in automated vehicles, a critical skill for building long-term trusted experiences.

Key Contributions

Presents method to induce driver emotions
Studies emotional reactions of driver-passenger dyads
Develops a reliable emotion labeling process
Achieves expert consensus on emotion labels
Provides methodology for emotional understanding

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Citation

@INPROCEEDINGS{8373886, author={Alyuz, Nese and Aslan, Sinem and Healey, Jennifer and Alvarez, Ignacio J. and Esme, Asli Arslan}, booktitle={2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)}, title={Towards Understanding Emotional Reactions of Driver-Passenger Dyads in Automated Driving}, year={2018}, volume={}, number={}, pages={585-592}, keywords={Labeling;Vehicles;Data collection;Cameras;Automation;Roads;affective computing;automated driving;drive simulation;labeling;inter-rater agreement}, doi={10.1109/FG.2018.00093} }

Alvarez, I. (2018). . 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018).

Theo, take a right… uh… left: Conversational Route Negotiations with Autonomous Driving Assistants

Published in Adjunct Proceedings of the 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’18), ACM 2018

Abstract

We foresee conversational driver assistants playing a crucial role in automated driving interactions. In this video we present a study of user interactions with an in-vehicle agent, “Theo”, under SAE Level 4 automated driving. We use a remote Wizard-of-Oz setup where participants, sitting in a driving simulator, experience real-life video footage transmitted from a vehicle in the neighborhood and interact with Theo to instruct the vehicle where to go. We configured Theo to present 3 levels of conversational abilities (terse, verbose and helpful). We show the results of 9 participants tasked to negotiate destinations and route changes. Voice interaction was reported as preferred means of communication with Theo. There was a clear preference for talkative assistants which were perceived more responsive and intelligent. We highlight challenging interactions for users such as vehicle maneuvers in parking areas and specifying drop off points and interesting associations between the agent performance and the automated vehicle abilities.

Key Contributions

Automated driving system development
Pioneers a remote AI Wizard-of-Oz setup
Tests three conversational agent styles
Finds user preference for talkative agents
Identifies challenging user interaction scenarios
Links agent performance to perceived safety

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Citation

@inproceedings{10.1145/3239092.3267414, author = {Ekandem, Joshua E. and Alvarez, Ignacio and Rayburn, Cat and Johnson, Andrea}, title = {Theo, take a right ... uh ... left: Conversational Route Negotiations with Autonomous Driving Assistants}, year = {2018}, isbn = {9781450359474}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3239092.3267414}, doi = {10.1145/3239092.3267414}, booktitle = {Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages = {261–262}, numpages = {2}, keywords = {Advanced Driver Assistance Systems, Automated Driving, Conversational Dialogues, Natural Language Understanding, Route-Based Negotiation}, location = {Toronto, ON, Canada}, series = {AutomotiveUI '18} }

Ekandem, Joshua E. and Alvarez, Ignacio and Rayburn, Cat and Johnson, Andrea (2018). Theo, take a right... uh... left: Conversational Route Negotiations with Autonomous Driving Assistants. Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

“Peace of Mind”, An Experiential Safety Framework for Automated Driving Technology Interactions

Published in Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), IEEE 2018

Abstract

Current automated driving systems assume drivers continuously monitor the vehicle. Meanwhile, fully automated vehicles aim at not requiring human intervention for their safely operation. The industry is currently debating how these novel systems can be certified under functional safety standards. In this paper, we argue that the current safety picture is not comprehensive enough, since it alienates users. We propose experiential safety as a complement to existing functional safety and to develop a framework for experiential safety interactions between the user and automation in automated driving environments. To support the experiential safety design model, we provide an overview of the user-centered research on experiential automation safety, which includes results from online surveys, personal interviews, and gamified group workshops. We explore current user behaviors by focusing on what makes them feel safe as drivers and passengers, and how unexpected events and automation responses might impact their perception of safety. Among the highlighted results, we show how mismatched expectations and unexpected behaviors from autonomous vehicles can lead to frustration and compromised trust. We also show how automation feedback to the user can generate stress and anxiety if not properly configured and how a cooperative relationship between automation and the driver leads to more satisfying driving experiences. Finally, we present guidelines for the experiential safety to be applied by automotive engineers and designers in their development of automated driving technologies.

Key Contributions

Proposes an experiential safety concept for AVs
Develops a novel gamified workshop method
Presents an experiential safety framework
Defines user-lenses and machine-lenses guidelines
Finds collaborative feedback improves user emotion

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Citation

@INPROCEEDINGS{8569686, author={Strano, Marina and Novak, Fabricio and Walbert, Shelly and Palmeiro, Beatriz and Morales, Sonia and Alvarez, Ignacio}, booktitle={2018 21st International Conference on Intelligent Transportation Systems (ITSC)}, title={“Peace of Mind”, An Experiential Safety Framework for Automated Driving Technology Interactions}, year={2018}, volume={}, number={}, pages={53-59}, keywords={Safety;Automation;Interviews;Automobiles;Guidelines;Task analysis;Automated Driving;Experiential Safety;User experience design;design research;design theory;design science;methodology design;emerging technology}, doi={10.1109/ITSC.2018.8569686} }

Alvarez, I. (2018). . 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

Emotional GaRage: A workshop on in-car emotion recognition and regulation

Published in ‘Adjunct Proceedings of the 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications AutomotiveUI 2018’, ACM 2018

Abstract

In-car emotion detection and regulation have become an emerging and important branch of research within the automotive domain. Different emotional states can greatly influence human driving performance and user experience both in manual and automated driving conditions. The monitoring and regulation of relevant emotional states is therefore important to avoid critical driving scenarios with the human driver being in charge, and to ensure comfort and acceptance in autonomous driving. In this workshop we want to discuss the empathic user interface research to address challenges and opportunities and to reveal new research directions for future work. This workshop provides a forum for exchange and discussion on empathic user interfaces, including methods for emotion recognition and regulation, empathic automotive human-machine interaction design, user evaluation and measurements, and subsequent improvement of autonomous driving experience.

Key Contributions

Discusses empathic in-car user interfaces
Identifies critical scenarios and emotions
Explores emotion recognition and regulation methods
Uses 4mat system for workshop structure
Creates roadmap for future research

Augmented reality (AR) has the potential to improve road safety, support more immersive (non-) driving related activities, and finally enhance driving experience. AR may also be the enabling technology to help on the transition towards automated driving. However, augmented reality still faces a number of technical challenges when applied in vehicles, and also several human factors issues need to be solved. In this workshop, we will discuss potential and constraints as well as impact, role, and adequacy of AR in driving applications. The primary goal of this workshop is to define a research agenda for the use of AR in intelligent vehicles within the next 3 to 5 years.

Key Contributions

Workshop organization and community building
Defines a 3-5 year collaborative research agenda
Identifies four key AR problem fields
Discusses practical usage of AR devices
Outlines research challenges and hypotheses

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Citation

@inproceedings{10.1145/3239092.3239096, author = {Riener, Andreas and Kun, Andrew L. and Gabbard, Joe and Brewster, Stephen and Riegler, Andreas}, title = {ARV 2018: 2nd Workshop on Augmented Reality for Intelligent Vehicles}, year = {2018}, isbn = {9781450359474}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3239092.3239096}, doi = {10.1145/3239092.3239096}, booktitle = {Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages = {30–36}, numpages = {7}, keywords = {Augmented reality, WSDs/HUDs, automated driving}, location = {Toronto, ON, Canada}, series = {AutomotiveUI '18} }

Riener, Andreas and Kun, Andrew L. and Gabbard, Joe and Brewster, Stephen and Riegler, Andreas (2018). ARV 2018: 2nd Workshop on Augmented Reality for Intelligent Vehicles. Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

An international survey on automated and electric vehicles: Austria, Germany, South Korea, and USA

Published in ‘Springer Natural Digital Human Modelling’, Springer 2018

Abstract

As development of automated vehicles and adoption of electric vehicles continue to grow, there is an increasing interest in the public opinions on these technologies. We conducted an international online survey to gather information about people’s hopes and concerns for automated and electric vehicles from a total of 866 people from four countries – Austria, Germany, South Korea, and USA. Results revealed some differences across countries in the perceptions of automated and electric vehicles. However, differences between the same countries have shrunk compared to our previous survey completed in 2012. Results are discussed with limitations and future work.

Key Contributions

Automated driving system development
Electric Vehicles
International Trends

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Citation

@inproceedings{jeon2018international, title={An international survey on automated and electric vehicles: Austria, Germany, South Korea, and USA}, author={Jeon, Myounghoon and Riener, Andreas and Sterkenburg, Jason and Lee, Ju-Hwan and Walker, Bruce N and Alvarez, Ignacio}, booktitle={International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management}, pages={579--587}, year={2018}, organization={Springer} }

Jeon, Myounghoon and Riener, Andreas and Sterkenburg, Jason and Lee, Ju-Hwan and Walker, Bruce N and Alvarez, Ignacio (2018). An international survey on automated and electric vehicles: Austria, Germany, South Korea, and USA. International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management.

2017

Workshop on user-centered design for automated driving systems

Published in Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, ACM 2017

Abstract

Automated driving systems (ADS) are mainly regarded from an innovation and technology-centered perspective. In academia, as well as in industry, there is a concentration on technical issues to maintain competitiveness while aspects like acceptance, trust and user experience are widely under-researched. However, the “human factor” is critical for a comprehensive establishment of ADS technology on the market. We believe that there is a need to focus on a user-centered design (UCD) perspective to bring ADS innovation to a next level and to achieve a wide acceptance in society. In this workshop we want to discuss special requirements of UCD applied to ADS, to address challenges and opportunities and to reveal new research fields for future work.

Key Contributions

Proposes migration-tolerant ATC display concept
Advocates for stepwise functionality introduction
Compares small versus large integration steps
Measures usability, learnability, and acceptance

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Citation

@inproceedings{10.1145/3131726.3131734, author = {Frison, Anna-Katharina and Pfleging, Bastian and Riener, Andreas and Jeon, Myounghoon Philart and Alvarez, Ignacio and Ju, Wendy}, title = {Workshop on User-Centered Design for Automated Driving Systems}, year = {2017}, isbn = {9781450351515}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3131726.3131734}, doi = {10.1145/3131726.3131734}, booktitle = {Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications Adjunct}, pages = {22–27}, numpages = {6}, keywords = {User-centered design, Automotive user interfaces, Automated driving systems}, location = {Oldenburg, Germany}, series = {AutomotiveUI '17} }

Alvarez, I. (2017). . Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications Adjunct.

Towards Adaptive Ambient In-Vehicle Displays and Interactions: Insights and Design Guidelines from the 2015 AutomotiveUI Dedicated Workshop

Published in ‘Automotive User Interfaces: Creating Interactive Experiences in the Car’, ACM 2017

Abstract

Informing a driver of a vehicle’s changing state and environment is a major challenge that grows with the introduction of in-vehicle assistant and infotainment systems. Even in the age of automation, the human will need to be in the loop for monitoring, taking over control, or making decisions. In these cases, poorly designed systems could lead to needless attentional demands imparted on the driver, taking it away from the primary driving task. Existing systems are offering simple and often unspecific alerts, leaving the human with the demanding task of identifying, localizing, and understanding the problem. Ideally, such systems should communicate information in a way that conveys its relevance and urgency. Specifically, information useful to promote driver safety should be conveyed as effective calls for action, while information not pertaining to safety (therefore less important) should be conveyed in ways that do not jeopardize driver attention. Adaptive ambient displays and peripheral interactions have the potential to provide superior solutions and could serve to unobtrusively present information, to shift the driver’s attention according to changing task demands, or enable a driver to react without losing the focus on the primary task. In order to build a common understanding across researchers and practitioners from different fields, we held a Workshop on Adaptive Ambient In-Vehicle Displays and Interactions at the AutomotiveUI`15 conference. In this chapter, we discuss the outcomes of this workshop, provide examples of possible applications now or in the future and conclude with challenges in developing or using adaptive ambient interactions.

Key Contributions

Workshop organization and community building
Human-computer interaction design

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Citation

@Inbook{Löcken2017, author={Löcken, Andreas and Sadeghian Borojeni, Shadan and Müller, Heiko and Gable, Thomas M. and Triberti, Stefano and Diels, Cyriel and Glatz, Christiane and Alvarez, Ignacio and Chuang, Lewis and Boll, Susanne}, editor={Meixner, Gerrit and Müller, Christian}, title={Towards Adaptive Ambient In-Vehicle Displays and Interactions: Insights and Design Guidelines from the 2015 AutomotiveUI Dedicated Workshop}, booktitle={Automotive User Interfaces: Creating Interactive Experiences in the Car}, year={2017}, publisher={Springer International Publishing}, address={Cham}, pages={325--348}, isbn={978-3-319-49448-7}, doi={10.1007/978-3-319-49448-7_12}, url={https://doi.org/10.1007/978-3-319-49448-7_12} }

Löcken, A., Sadeghian Borojeni, S., Müller, H., Gable, T. M., Triberti, S., Diels, C., Glatz, C., Alvarez, I., Chuang, L., & Boll, S. (2017). Towards Adaptive Ambient In-Vehicle Displays and Interactions: Insights and Design Guidelines from the 2015 AutomotiveUI Dedicated Workshop. In G. Meixner & C. Müller (Eds.), Automotive User Interfaces: Creating Interactive Experiences in the Car (pp. 325-348). Springer International Publishing.

The insight–prototype–product cycle best practices and processes to iteratively advance in-vehicle interactive experiences development

Published in Automotive User Interfaces: Creating Interactive Experiences in the Car, Springer Book, Springer 2017

Abstract

In-vehicle experiences are made up mainly of mundane small moments, repeated practices, and taken-for-granted decisions that make up daily experiences in and around private passenger vehicles. Understanding what those experiences are for drivers around the world presents an opportunity for designing novel interactive experiences, technologies, and user interfaces for vehicles. In this chapter, we present a set of tools, methodologies, and practices that will help reader create a holistic design space for future mobility. Transitioning between ethnography, insights, prototyping, experience design, and requirements decomposition is a challenging task even for experienced UX professionals. This chapter provides guidance in this matter with practical examples.

Key Contributions

Ethnographic research methods for Automotive System Development
Rapid prototyping methods for Automotive Functions
Experience Design mapping to product specifications
Product Development evaluation and validation
Product Success metrics

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Citation

@incollection{alvarez2017insight, title={The insight--prototype--product cycle best practices and processes to iteratively advance in-vehicle interactive experiences development}, author={Alvarez, Ignacio and Jordan, Adam and Knopf, Juliana and LeBlanc, Darrell and Rumbel, Laura and Zafiroglu, Alexandra}, booktitle={Automotive User Interfaces: Creating Interactive Experiences in the Car}, pages={377--400}, year={2017}, publisher={Springer} }

Alvarez, I. (2017). . Automotive User Interfaces: Creating Interactive Experiences in the Car.

Socializing under the influence of distracted driving: a study of the effects of in-vehicle and outside-of-the-vehicle communication while driving

Published in ‘Springer Nature Advances in Human Aspects of Transportation’, Springer 2017

Abstract

Advancements of in-vehicle technologies and the development of mobile applications that keep a driver connected in a driving environment have caused an increasingly dangerous safety concern. Distracted driving has gained the attention of legislators and governments globally. Countries have constituted bans that partially or fully forbid drivers from using gadgets while driving, especially hindering out-of-the-vehicle communications. This paper introduces Voiceing™, a voice-activated application meant to improve social communications in the car, serving as a safe alternative to distracted driving. Other modalities of interaction such as texting, in-vehicle conversations and outside-of-the-vehicle conversation have been measured and compared with Voiceing™ investigating effects on driver’s performance, cognitive load and user acceptance. Results from this study suggest that Voiceing™ is a safer alternative than in-vehicle interactions with humans. Results also show that natural speech interaction of in-vehicle applications and the inclusion of context awareness help improve driving performance while interacting with a vehicle system.

Key Contributions

Introduces Voiceing™ a voice-activated application
Compares various in-car communication methods
Measures performance, cognitive load, acceptance
Voiceing™ is safer than human interactions
Natural speech improves driving performance

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Citation

@inproceedings{alnizami2016socializing, title={Socializing under the influence of distracted driving: a study of the effects of in-vehicle and outside-of-the-vehicle communication while driving}, author={Alnizami, Hanan and Alvarez, Ignacio and Gilbert, Juan E}, booktitle={Advances in Human Aspects of Transportation: Proceedings of the AHFE 2016 International Conference on Human Factors in Transportation, July 27-31, 2016, Walt Disney World{\textregistered}, Florida, USA}, pages={243--255}, year={2016}, organization={Springer} }

Alnizami, Hanan and Alvarez, Ignacio and Gilbert, Juan E (2016). Socializing under the influence of distracted driving: a study of the effects of in-vehicle and outside-of-the-vehicle communication while driving. Advances in Human Aspects of Transportation: Proceedings of the AHFE 2016 International Conference on Human Factors in Transportation, July 27-31, 2016, Walt Disney World{\textregistered.

Skyline: A Platform Towards Scalable UX-Centric In-Vehicle HMI Development

Published in ‘International Journal of Mobile Human Computer Interaction (IJMHCI)’, 2017

Abstract

This paper describes the research and development process of an in-vehicle user experience using Skyline, an automotive prototyping platform created in Intel Labs to empower interaction designers and user experience researches to rapidly and iteratively develop and test in-vehicle user experience concepts. The paper describes the hardware and software components of Skyline in depth and how to configure them to suit individual researcher needs. The paper also presents a case study to exemplify the design making process that Skyline enables. From ideation to use-case creation, prototyping and validation through user assessment, the paper showcases the benefits of capturing early qualitative user feedback as support for rapid prototyping walking through a study titled Agency vs. Control and the associated interactions inside the cockpit. Ten defined use-cases are developed and integrated into a hero scenario in Skyline. High fidelity HMI concepts are tested and validated over the course of six months with feedback from a total of fifty users.

Key Contributions

Automotive HMI Prototyping
Rapid HCI User Interface Design
Evaluation Methods

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Citation

@article{alvarez2017skyline, title={Skyline: A Platform Towards Scalable UX-Centric In-Vehicle HMI Development}, author={Alvarez, Ignacio and Rumbel, Laura}, journal={International Journal of Mobile Human Computer Interaction (IJMHCI)}, volume={9}, number={3}, pages={34--53}, year={2017}, publisher={IGI Global Scientific Publishing} }

Alvarez, Ignacio and Rumbel, Laura (2017). Skyline: A Platform Towards Scalable UX-Centric In-Vehicle HMI Development. International Journal of Mobile Human Computer Interaction (IJMHCI).

Driver in the loop: Best practices in automotive sensing and feedback mechanisms

Published in ‘Automotive user interfaces: creating interactive experiences in the car’, 2017

Abstract

Given the rapid advancement of technologies in the automotive domain, driver–vehicle interaction has recently become more and more complicated. The amount of research applied to the vehicle cockpit is increasing, with the advent of (highly) automated driving, as the range of interaction that is possible in a driving vehicle expands. However, as opportunities increase, so does the number of challenges that automotive user experience designers and researchers will face. This chapter focuses on the instrumentation of sensing and displaying techniques and technologies to make better user experience while driving. In the driver–vehicle interaction loop, the vehicle can sense driver states, analyze, estimate, and model the data, and then display it through the appropriate channels for intervention purposes. To improve the interaction, a huge number of new/affordable sensing (EEG, fNIRS, IR imaging) and feedback (head-up displays, auditory feedback, tactile arrays, etc.) techniques have been introduced. However, little research has attempted to investigate this area in a systematic way. This chapter provides an overview of recent advances of input and output modalities to be used for timely, appropriate driver–vehicle interaction. After outlining relevant background, we provide information on the best-known practices for input and output modalities based on the exchange results from the workshop on practical experiences for measuring and modeling drivers and driver–vehicle interactions at AutomotiveUI 2015. This chapter can help answer research questions on how to instrument a driving simulator or realistic study to gather data and how to place interaction outputs to enable appropriate driver interactions.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

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Citation

@incollection{riener2017driver, title={Driver in the loop: Best practices in automotive sensing and feedback mechanisms}, author={Riener, Andreas and Jeon, Myounghoon and Alvarez, Ignacio and Frison, Anna K}, booktitle={Automotive user interfaces: creating interactive experiences in the car}, pages={295--323}, year={2017}, publisher={Springer} }

Riener, Andreas and Jeon, Myounghoon and Alvarez, Ignacio and Frison, Anna K (2017). Driver in the loop: Best practices in automotive sensing and feedback mechanisms. Automotive user interfaces: creating interactive experiences in the car.

9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proceedings

Published in ‘Proceeding of Automotive UI 2017’, ACM 2017

Abstract

Abstract not yet available.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

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BibTeX

2016

Autonomous hmi made easy: Prototyping reactive in-cabin aware hmis

Published in ‘Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’16)’, ACM 2016

Abstract

Automotive user experiences can be increasingly personalized and adaptive thanks to advances in in-vehicle sensors and user modelling but current automotive software development frameworks still require large software development efforts to create custom interaction solutions. In this paper we propose a novel system architecture aimed at supporting automotive researchers and designers by simplifying the prototyping process towards novel adaptive user interfaces. We describe the integration of RealSense sensors and the Context Sensing SDK with the Skyline driving simulator framework. The combination of these tools allows rapid prototyping of in-cabin context aware interactions. The paper presents two use cases of in-cabin-aware prototypes, a user profile loading interface that recognizes identities and occupant roles and an L4 to L3 take-over control interface using RealSense and Context sensing APIs to detect in-vehicle events and Skyline to present real-time adaptive warning interfaces. The resulting experiences are core components of an intelligent ADAS framework for research of IVI personalization and highly automated collaborative driving.

Key Contributions

Automated driving system development
Proposes architecture integrating Skyline and sensors
Simplifies prototyping of adaptive HMIs
Demonstrates automatic user profile recognition
Prototypes context-aware take-over interface
Integrates sensor SDKs with HMI platform

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Citation

@inproceedings{10.1145/3004323.3004353, author = {Rivera, Victor Palacios and Rumbel, Laura and Alvarez, Ignacio}, title = {Autonomous HMI Made Easy: Prototyping Reactive In-cabin Aware HMIs}, year = {2016}, isbn = {9781450346542}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3004323.3004353}, doi = {10.1145/3004323.3004353}, booktitle = {Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages = {1–7}, numpages = {7}, keywords = {ADAS, Adaptive Interface, Ambient Intelligence, Automotive User Experience, Context Sensing, Context-Awareness, Rapid Prototyping, RealSense, Skyline}, location = {Ann Arbor, MI, USA}, series = {AutomotiveUI '16 Adjunct} }

Rivera, Victor Palacios and Rumbel, Laura and Alvarez, Ignacio (2016). Autonomous hmi made easy: Prototyping reactive in-cabin aware hmis. Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

Automotive user interfaces in the age of automation (Dagstuhl Seminar 16262)

Published in Dagstuhl Reports, Dagtsuhl 2016

Abstract

The next big change in the automotive domain will be the move towards automated and semi-automated driving. We can expect an increasing level of autonomous driving in the coming years, resulting in new opportunities for the car as an infotainment platform when standard driving tasks will be automated. This change also comes with a number of challenges to automotive user interfaces. Core challenges for the assistance system and the user interface will be distributing tasks between the assistance system and the driver, the re-engagement of drivers in semi-automated driving back to the driving task, and collaborative driving in which cars collectively work together (e.g., platoons). Overall, in the coming years we will need to design interfaces and applications that make driving safe while enabling communication, work, and play in human-operated vehicles. This Dagstuhl seminar brought together researchers from human computer interaction, cognitive psychology, human factors psychology and also from automotive industry and OEMs to discuss the new interface paradigms for (semi-)automated driving.

Key Contributions

Identifies three core research challenges
Explores future non-driving in-car activities
Analyzes the take-over/handover problem
Proposes methods for calibrating trust
Creates framework for classifying research methods

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Citation

@Article{riener_et_al:DagRep.6.6.111, author = {Riener, Andreas and Boll, Susanne and Kun, Andrew L.}, title = {Automotive User Interfaces in the Age of Automation (Dagstuhl Seminar 16262)}, pages = {111--157}, journal = {Dagstuhl Reports}, ISSN = {2192-5283}, year = {2016}, volume = {6}, number = {6}, editor = {Riener, Andreas and Boll, Susanne and Kun, Andrew L.}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.6.6.111}, URN = {urn:nbn:de:0030-drops-67582}, doi = {10.4230/DagRep.6.6.111}, annote = {Keywords: Automotive UIs; Driver-vehicle interaction services; UX in driving; Customization of vehicles/UIs; (Over)trust; Ethical issues} }

Alvarez, I. (2016). . Dagstuhl Reports.

1st workshop on situational awareness in semi-automated vehicles

Published in Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, ACM 2016

Abstract

This workshop will focus on the problem of occupant and vehicle situational awareness with respect to automated vehicles when the driver must take over control. It will explore the future of fully automated and mixed traffic situations where vehicles are assumed to be operating at level 3 or above. In this case, all critical driving functions will be handled by the vehicle with the possibility of transitions between manual and automated driving modes at any time. This creates a driver environment where, unlike manual driving, there is no direct intrinsic motivation for the driver to be aware of the traffic situation at all times. Therefore, it is highly likely that when such a transition occurs, the driver will not be able to transition either safely or within an appropriate period of time. This workshop will address this challenge by inviting experts and practitioners from the automotive and related domains to explore concepts and solutions to increase, maintain and transfer situational awareness in semi-automated vehicles.

Key Contributions

Addresses driver situational awareness problem
Focuses on take-over and handover situations
Explores shared situational awareness concepts
Applies Endsley’s model of SA
Considers long-term driver deskilling effects

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@inproceedings{10.1145/3004323.3005688, author = {McCall, Rod and Baumann, Martin and Politis, Ioannis and Borojeni, Shadan Sadeghian and Alvarez, Ignacio and Mirnig, Alexander and Meschtscherjakov, Alexander and Tscheligi, Manfred and Chuang, Lewis and Terken, Jacques}, title = {1st Workshop on Situational Awareness in Semi-Automated Vehicles}, year = {2016}, isbn = {9781450346542}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3004323.3005688}, doi = {10.1145/3004323.3005688}, booktitle = {Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages = {233–236}, numpages = {4}, keywords = {Situational Awareness, Context Awareness, Cognitive Load, Automated Driving}, location = {Ann Arbor, MI, USA}, series = {AutomotiveUI '16 Adjunct} }

Alvarez, I. (2016). . Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

1st workshop on ethically inspired user interfaces for automated driving

Published in * Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications*, ACM 2016

Abstract

On July 1st 2016, the first automated vehicle fatality became headline news [9] and caused a nationwide wave of concern. Now we have at least one situation in which a controlled automated vehicle system failed to detect a life threatening situation. The question still remains: How can an autonomous system make ethical decisions that involve human lives? Control negotiation strategies require prior encoding of ethical conventions into decision making algorithms, which is not at all an easy task – especially considering that actually coming up with ethically sound decision strategies in the first place is often very difficult, even for human agents. This workshop seeks to provide a forum for experts across different backgrounds to voice and formalize the ethical aspects of automotive user interfaces in the context of automated driving. The goal is to derive working principles that will guide shared decision-making between human drivers and their automated vehicles.

Key Contributions

Discusses ethical dilemmas in automated driving
Analyzes the limits of Asimov’s Automation Laws
Questions human vs. machine decision-making
Addresses cultural differences in moral norms
Seeks to formalize ethics for algorithms

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@inproceedings{10.1145/3004323.3005687, author = {Riener, Andreas and Jeon, Myounghoon Philart and Alvarez, Ignacio and Pfleging, Bastian and Mirnig, Alexander and Tscheligi, Manfred and Chuang, Lewis}, title = {1st Workshop on Ethically Inspired User Interfaces for Automated Driving}, year = {2016}, isbn = {9781450346542}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3004323.3005687}, doi = {10.1145/3004323.3005687}, booktitle = {Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages = {217–220}, numpages = {4}, keywords = {User acceptance and trust, Trolley problem, Negotiation algorithms, Driver-vehicle interfaces, Decision making, Automated driving, Asimov's laws}, location = {Ann Arbor, MI, USA}, series = {AutomotiveUI '16 Adjunct} }

Alvarez, I. (2016). . Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

2015

Workshop on Practical Experiences in Measuring and Modeling Drivers and Driver-Vehicle Interactions

Published in Proceedings of Automotive UI 2015, ACM 2015

Abstract

The search term “driver-vehicle interaction study” results in2,690 Google Scholar hits of research papers published inthe past 5 years (2010-2015). This huge number clearlypoints out the problem that researchers (particularly, new tothis field) are exposed to, namely that many decisionsregarding the setting, (e.g., lab/field, low-/high-fidelitysimulator, within/between subjects, sample size, biasedsubject, learning effect, sensor technology, mobilehardware, synchronization issues, briefing, etc.) have to beestablished early in the design phase without the referenceof principled guidelines and best practices to support themin identifying the optimal solution to answer their researchquestion of interest. This workshop invites a) people activein the field to share their experiences in executing studies tomeasure driver behavior or vehicle conditions (driver-vehicle interactions), and b) young researchers to draftresearch questions, present their problems, and discusspossible solutions with the other participants.

Key Contributions

Addresses lack of research best practices
Focuses on modeling driver, driving, interactions
Connects new and experienced researchers
Reviews sensors for user modeling
Discusses experimental methods and settings

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@inproceedings{riener2015workshop, title={Workshop on Practical Experiences in Measuring and Modeling Drivers and Driver-Vehicle Interactions}, author={Riener, Andreas and Alvarez, Ignacio and Chuang, Lewis and Ju, Wendy and Pfleging, Bastian and Chiesa, Mario}, booktitle={Workshop on Practical Experiences in Measuring and Modeling Drivers and Driver-Vehicle Interactions In conjunction with AutomotiveUI 2015}, pages={1--4}, year={2015} }

Alvarez, I. (2015). . Workshop on Practical Experiences in Measuring and Modeling Drivers and Driver-Vehicle Interactions In conjunction with AutomotiveUI 2015.

Workshop on Adaptive Ambient In-Vehicle Displays and Interactions

Published in Proceedings of Automotive UI 2015, ACM 2015

Abstract

A major challenge in today’s as well as future driving is to keep drivers informed about the vehicle’s state and the environment. Today’s assistant and infotainment systems compete for the drivers’ attention and may even distract them from the primary driving task. Further, with an increase in automation, the vehicle needs to be able to communicate information with different urgency levels. While some information are not important and should never distract a driver from important tasks, there are also calls for action, which a driver should not be able to ignore. We believe in adaptive ambient displays and peripheral interaction as one possible way to unobtrusively present information while being able to switch the driver’s attention if needed. In this workshop the focus lies in exchange of best known methods and discussion on challenges and potentials for this kind of interaction in today’s scenarios as well as in future mixed or full autonomous traffic. The central objective of this workshop is to bring together researchers from different domains and discuss radical, innovative, and engaging ideas and a future landscape for research in this area.

Key Contributions

Proposes workshop on adaptive ambient displays
Focuses on ambient and peripheral interaction
Addresses managing driver attention shifts
Considers non- to fully-autonomous driving
Fosters interdisciplinary discussion on UIs.

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@inproceedings{locken2015workshop, title={Workshop on Adaptive Ambient In-Vehicle Displays and Interactions}, author={L{"o}cken, Andreas and Borojeni, Shadan Sadeghian and Chuang, Lewis and Schroeter, Ronald and Alvarez, Ignacio and Meijering, Valerian and Rover, Jaguar Land}, booktitle={Workshop on Adaptive Ambient In-Vehicle Displays and Interactions In conjunction with Automotive'UI 2015 (WAADI'15)}, pages={1--4}, year={2015} }

Alvarez, I. (2015). . Workshop on Adaptive Ambient In-Vehicle Displays and Interactions In conjunction with Automotive'UI 2015 (WAADI'15).

Skyline: a rapid prototyping driving simulator for user experience

Published in ‘AutomotiveUI 2015’, 2015

Abstract

The search term “driver-vehicle interaction study” results in 2,690 Google Scholar hits of research papers published in the past 5 years (2010-2015). This huge number clearly points out the problem that researchers (particularly, new to this field) are exposed to, namely that many decisions regarding the setting, (e.g., lab/field, low-/high-fidelity simulator, within/between subjects, sample size, biased subject, learning effect, sensor technology, mobile hardware, synchronization issues, briefing, etc.) have to be established early in the design phase without the reference of principled guidelines and best practices to support the min identifying the optimal solution to answer their research question of interest. This workshop invites a) people activein the field to share their experiences in executing studies to measure driver behavior or vehicle conditions (driver-vehicle interactions), and b) young researchers to draft research questions, present their problems, and discuss possible solutions with the other participants.

Key Contributions

Addresses lack of research best practices
Focuses on modeling driver, driving, interactions
Connects new and experienced researchers
Reviews sensors for user modeling
Discusses experimental methods and settings

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Citation

@inproceedings{riener2015workshop, title={Workshop on Practical Experiences in Measuring and Modeling Drivers and Driver-Vehicle Interactions}, author={Riener, Andreas and Alvarez, Ignacio and Chuang, Lewis and Ju, Wendy and Pfleging, Bastian and Chiesa, Mario}, booktitle={Workshop on Practical Experiences in Measuring and Modeling Drivers and Driver-Vehicle Interactions In conjunction with AutomotiveUI 2015}, pages={1--4}, year={2015} }

Riener, Andreas and Alvarez, Ignacio and Chuang, Lewis and Ju, Wendy and Pfleging, Bastian and Chiesa, Mario (2015). Skyline: a rapid prototyping driving simulator for user experience. Workshop on Practical Experiences in Measuring and Modeling Drivers and Driver-Vehicle Interactions In conjunction with AutomotiveUI 2015.

Report on the in-vehicle auditory interactions workshop: Taxonomy, challenges, and approaches

Published in ‘Adjunct Proceedings AutomotiveUI ’15’, ACM 2015

Abstract

As driving is mainly a visual task, auditory displays play a critical role for in-vehicle interactions. To improve invehicle auditory interactions to the advanced level, auditory display researchers and automotive user interface researchers came together to discuss this timely topic at an in-vehicle auditory interactions workshop at the International Conference on Auditory Display (ICAD). The present paper reports discussion outcomes from the workshop for more discussions at the AutoUI conference.

Key Contributions

Workshop organization and community building
Designed sounds for automated driving take-overs
Proposed interactive music for eco-driving
Considered auditory displays for passengers
Taxonomized auditory collision warning issues
Bridged auditory and automotive UI communities

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@inproceedings{jeon2015report, title={Report on the in-vehicle auditory interactions workshop: Taxonomy, challenges, and approaches}, author={Jeon, Myounghoon and Hermann, Thomas and Bazilinskyy, Pavlo}, booktitle={Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications-Automotive’UI}, volume={15}, year={2015} }

Jeon, Myounghoon and Hermann, Thomas and Bazilinskyy, Pavlo (2015). Report on the in-vehicle auditory interactions workshop: Taxonomy, challenges, and approaches. Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications-Automotive’UI.

Prototyping adaptive automotive UX: A design pedagogy approach

Published in ‘Extended Proceedings of the 7th International Conference on Automotive User’, 2015

Abstract

This paper outlines and evaluates experiential prototyping for emerging vehicle UX design within a pedagogical framework. Drawing from studio experience, we discuss the learnings, options and risks that in-vehicle UX designers face in prototyping realtime, adaptive user interfaces, and suggest methods and solutions for designers wishing to expand their creative practice.

Key Contributions

Presents a novel pedagogical framework for UX
Applies “design thinking through making”
Outlines low-tech experiential prototyping methods
Advocates for Minimum Viable Prototypes
Identifies risks in UX prototyping

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Citation

@inproceedings{hendrie2015prototyping, title={Prototyping adaptive automotive UX: A design pedagogy approach}, author={Hendrie, Maggie and Alvarez, Ignacio and Hooker, Ben}, booktitle={Extended Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications—AutomotiveUI}, volume={15}, year={2015} }

Hendrie, Maggie and Alvarez, Ignacio and Hooker, Ben (2015). Prototyping adaptive automotive UX: A design pedagogy approach. Extended Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications—AutomotiveUI.

Help on the road: Effects of vehicle manual consultation in driving performance across modalities

Published in ‘International journal of human-computer studies’, Elsevier 2015

Abstract

The growing advancements of in-vehicle electronics and the intrusion of consumer electronics in the vehicle cockpit have increased the complexity of in-car experiences. Therefore, vehicle manuals are needed, now more than ever, to provide information and guidance. Automakers have extended user assistance through multimedia, integrated manuals, online services and telephonic assistance. However, no driver-centric interfaces have been created to provide vehicle documentation assistance effectively. Drivers are expected to interrupt the driving experience in order to find vehicle information in a paper manual. This paper compares the effects on driving performance and cognitive load when consulting a manual in a simulated driving environment through various conditions. These conditions consist of interacting with a voice activated vehicle manual called the Voice User Help, an on-board multimedia manual, a passenger, and a call center. Results suggest that any kind of interaction to access information while driving has an impact on the driver׳s attention based on a decrease in driving performance and increase of cognitive load. However, amongst all modalities, voice interfaces seem to be the better option for consulting information while driving. Also, and under some circumstances, interaction with a conversational manual system appears to be safer than human-to-human communication.

Key Contributions

Compared manual consultation methods while driving
Measured effects on driver performance
Assessed cognitive load across modalities
Found voice interfaces safer overall
Showed manual interfaces cause inattention

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Citation

@article{alvarez2015help, title={Help on the road: Effects of vehicle manual consultation in driving performance across modalities}, author={Alvarez, Ignacio and Alnizami, Hanan and Dunbar, Jerone and Jackson, France and Gilbert, Juan E}, journal={International journal of human-computer studies}, volume={73}, pages={19--29}, year={2015}, publisher={Elsevier} }

Alvarez, Ignacio and Alnizami, Hanan and Dunbar, Jerone and Jackson, France and Gilbert, Juan E (2015). Help on the road: Effects of vehicle manual consultation in driving performance across modalities. International journal of human-computer studies.

2014

The Social Car: Socially-inspired Mechanisms for Future Mobility Services

Published in ‘Pervasive and Mobile Computing’, 2014

Abstract

Research on next generation automotive ICT is challenged by the complex interactions of technological advancements and the social nature of individuals using and adopting technology. Traffic in the future will no longer be considered as a network of individually behaving “dumb” cars, but rather as the entirety of social interactions between its entities. Successful application of collective, socially inspired driving mechanisms requires to understand how socially-inspired vehicles (i.e., driver-car pairs) could make use of their social habitus, composed from (past and present) driving behavior, social interactions with pedestrians, vehicles, infrastructure, etc., and drivers’ vital states when exposed to other road participants in live traffic. In response to this emerging research direction, the aim of this workshop is to achieve a common understanding of the symbiosis between drivers, cars, and infrastructure from a global point of view (referred to as “collective driving”). In particular, this workshop is expected to provoke an active debate on the adequacy of the concept of socializing cars, addressing questions such as who can communicate what, when, how, and why?

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

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Citation

@article{rienerworkshop, title={WORKSHOP at AutoUI 2013 “Socially-inspired Mechanisms for Future Mobility Services”}, author={Riener, Andreas and Jeon, Myounghoon and Alvarez, Ignacio}, journal={Automotive User Interfaces and Interactive Vehicular Applications}, pages={75} }

Alvarez, I. (). The Social Car: Socially-inspired Mechanisms for Future Mobility Services. Automotive User Interfaces and Interactive Vehicular Applications.

Social, natural, and peripheral interactions: Together and separate

Published in ‘Proceedings of AutomotiveUI 2014’, 2014

Abstract

A major challenge in the future of traffic is to understand how “socially-aware vehicles” could be making use of their social habitus, formed by any information that can be inferred from past and present social relations, social interactions, and a driver’s social state when exposed to other participants in real, live traffic. The aim of this workshop in recognition of this challenge is to advance on a common understanding of the symbiosis between drivers, cars, and the infrastructure. The central objective of the workshop is to provoke an active debate on the adequacy of the concept of social, natural, and peripheral interaction, addressing questions such as “who can communicate what”, “when”, “how”, and “why”? To tackle these questions, we would like to collect different, radical, innovative, versatile, and engaging works that challenge or re-imagine human interactions in the near future automobile space.

Key Contributions

Human-computer interaction design

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Citation

@inproceedings{10.1145/2667239.2667282, author = {Riener, Andreas and Pfleging, Bastian and Jeon, Myhounghoon and Chiesa, Mario and Alvarez, Ignacio and L"{o}cken, Andreas and M"{u}ller, Heiko}, title = {Social, Natural, and Peripheral Interactions: Together and Separate}, year = {2014}, isbn = {9781450307253}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/2667239.2667282}, doi = {10.1145/2667239.2667282}, pages = {1–6}, numpages = {6}, keywords = {Automotive user interfaces, cognitive limits, human factors, human-centered design, individuality and personality, multimodal interaction, natural user interfaces (NUI), peripheral interaction (PI), social driving}, location = {Seattle, WA, USA}, series = {AutomotiveUI '14} }

Riener, Andreas and Pfleging, Bastian and Jeon, Myhounghoon and Chiesa, Mario and Alvarez, Ignacio and L"{o (2014). Social, natural, and peripheral interactions: Together and separate. .

Response to Letter by Padulo and Ardigò

Published in Jouarnal or Ergonomics, Taylor & Francis 2012

Abstract

Abstract not available

Key Contributions

Not available

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@article{Ekandem01022014, author = {Joshua I. Ekandem and Ignacio Alvarez and Melva T. James and Juan E. Gilbert and Timothy A. Davis}, title = {Response to Letter by Padulo and Ardigò}, journal = {Ergonomics}, volume = {57}, number = {2}, pages = {284--284}, year = {2014}, publisher = {Taylor \& Francis}, doi = {10.1080/00140139.2013.877599}, note ={PMID: 24517237}, URL = {https://doi.org/10.1080/00140139.2013.877599}, eprint = {https://doi.org/10.1080/00140139.2013.877599} }

Alvarez, I. (2014). . Ergonomics.

2012

The voice user help, a smart vehicle assistant for the elderly

Published in Proceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence, Springer 2012

Abstract

The rapid advancement of vehicular technologies has resulted in an exponential increase of new vehicle functions road assistance and connected vehicles services. Vehicle manuals are designed to provide support and information about the use and maintenance of these features. However, current vehicle user manuals do not allow looking for information under driving conditions. This paper presents the Voice User Help, a smart voice-operated system that utilizes natural language understanding and emotional adaptive interfaces to assist drivers when looking for vehicle information with minimal effect on their driving performance. Additionally, the system presents an opportunity for elder drivers to reduce the learning curve of new in-vehicle technologies and improve efficiency. Results on user acceptance of the Voice User Help, as well as cognitive load and driver distraction effects generated during a simulated drive indicate that the Voice User help is an extremely desirable feature and potentially safe application since it did not significantly decrement driving performance. Furthermore preliminary results on adaptive voice interfaces using emotion recognition indicate that personalization of the interaction will be able to palliate possible negative effects that happen during system error recovery.

Key Contributions

Developed voice-operated vehicle help system
Aimed to assist elderly drivers
Evaluated driver distraction and usability
Integrated emotion recognition for adaptation
Created a VUH-specific emotional taxonomy

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Citation

@inproceedings{alvarez2012voice, title={The voice user help, a smart vehicle assistant for the elderly}, author={Alvarez, Ignacio and L{\'o}pez-de-Ipi{\~n}a, Miren Karmele and Gilbert, Juan E}, booktitle={International Conference on Ubiquitous Computing and Ambient Intelligence}, pages={314--321}, year={2012}, organization={Springer} }

Alvarez, I. (2012). . International Conference on Ubiquitous Computing and Ambient Intelligence.

Evaluating the ergonomics of BCI devices for research and experimentation

Published in Journal of Ergonomics, Taylor & Francis Online 2012

Abstract

The use of brain computer interface (BCI) devices in research and applications has exploded in recent years. Applications such as lie detectors that use functional magnetic resonance imaging (fMRI) to video games controlled using electroencephalography (EEG) are currently in use. These developments, coupled with the emergence of inexpensive commercial BCI headsets, such as the Emotiv EPOC ( http://emotiv.com/index.php) and the Neurosky MindWave, have also highlighted the need of performing basic ergonomics research since such devices have usability issues, such as comfort during prolonged use, and reduced performance for individuals with common physical attributes, such as long or coarse hair. This paper examines the feasibility of using consumer BCIs in scientific research. In particular, we compare user comfort, experiment preparation time, signal reliability and ease of use in light of individual differences among subjects for two commercially available hardware devices, the Emotiv EPOC and the Neurosky MindWave. Based on these results, we suggest some basic considerations for selecting a commercial BCI for research and experimentation.

Statement of Relevance: Despite increased usage, few studies have examined the usability of commercial BCI hardware. This study assesses usability and experimentation factors of two commercial BCI models, for the purpose of creating basic guidelines for increased usability. Finding that more sensors can be less comfortable and accurate than devices with fewer sensors.

Key Contributions

BCI evaluation
BCI Signal Limitation Modelling
Practical Use recommendations

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Citation

@article{ekandem2012evaluating, title={Evaluating the ergonomics of BCI devices for research and experimentation}, author={Ekandem, Joshua I and Davis, Timothy A and Alvarez, Ignacio and James, Melva T and Gilbert, Juan E}, journal={Ergonomics}, volume={55}, number={5}, pages={592--598}, year={2012}, publisher={Taylor \& Francis} }

Alvarez, I. (2012). . Ergonomics.

Emotional Adaptive Vehicle User Interfaces, Moderating negative effects of failed technology interactions while driving

Published in ‘Adjunct Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI 2012)’, 2012

Abstract

Automotive Natural User Interfaces have the potential to increase user experience providing intuitive interactions for drivers. However, in the complex setting of a driving vehicle, failed interactions with in-vehicle technology can lead to frustration and put drivers in a dangerous situation. This paper evaluates the possibility of applying emotion recognition to vehicular spoken dialogue systems in order to adapt the dialog strategies, in error recovery scenarios. An emotional taxonomy is developed for the interactions with a conversational vehicular application, the Voice User Help. The positive results of the performance of VUH emotion recognizer support the creation of real-time classification of the user emotional state, which serves as basis to emotional reappraisal dialog strategies that mitigate negative effects on the driver’s cognitive load and driver performance.

Key Contributions

Developed the Voice User Help system
Created a VUH-specific emotional taxonomy
Built a real-time emotion recognizer
Proposed adaptive emotional dialog strategies
Used emotion for error recovery

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Citation

@inproceedings{alvarez2012emotional, title={Emotional Adaptive Vehicle User Interfaces: moderating negative effects of failed technology interactions while driving}, author={Alvarez, Ignacio and Lopez-de Ipi{\~n}a, Karmele and Daily, Shaundra B and Gilbert, Juan E}, booktitle={Proceedings of Workshop of Automotive Natural Interfaces, together with International Conference on Automotive User Interfaces}, pages={57--60}, year={2012} }

Alvarez, Ignacio and Lopez-de Ipi{\~n (2012). Emotional Adaptive Vehicle User Interfaces, Moderating negative effects of failed technology interactions while driving. Proceedings of Workshop of Automotive Natural Interfaces, together with International Conference on Automotive User Interfaces.

Contribution to the development of intelligent conversational assistants in automotive environments

Published in EHU Scientific Production Portal, EHU 2012

Abstract

This thesis presents the design and development of the Conversational Automotive Assistant, Voice User Help, a conversational system based on the question-answer paradigm and designed to consult vehicle documentation while driving. This work compiles research in the fields of technical documentation, information retrieval, natural language processing, vehicle user interface design, user experience, and affective computing with the goal of creating an adaptive and dynamic assistant that modifies its conversational behavior depending on the user’s emotional state.

Key Contributions

Conversational Asistant for In-cabin Automotive Applications
Information Retrieval Algorithms
Human-Computer Interaction Voice Interfaces
Automotive Context-aware Affective Computing

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Citation

@phdthesis{martinez2012contribution, title={Contribution to the development of intelligent conversational assistants in automotive environments}, author={Mart{\'\i}nez, Ignacio Javier {\'A}lvarez}, year={2012}, school={Universidad del Pa{\'\i}s Vasco-Euskal Herriko Unibertsitatea} }

Alvarez, I. (2012). . .

AutoNUI: 2nd Workshop on Automotive Natural User Interfaces

Published in ‘Adjunct Proceedings of the 4th International Conference on Automotive User Interfaces, ACM 2012

Abstract

Natural user interfaces—generally based on gesture and speech interaction—are an increasingly hot topic in research and are already being applied in a multitude of commercial products. Most use cases currently involve consumer electronics devices like smart phones, tablets, TV sets, game consoles, or large-screen tabletop computers.Motivated by the latest results in those areas, our vision is to apply natural user interfaces, for example gesture and conversational speech interaction, to the automotive domain as well. This integration might on one hand reduce driver distraction in certain cases and on the other hand might allow the design of new user experiences for infotainment and entertainment systems. The goal of this workshop is to explore the design space of natural multi-modal automotive user interfaces and to continue the fruitful discussions held at the 1st Workshop on Automotive Natural User Interfaces from AutomotiveUI ’11 in Salzburg, Austria. We would like to analyze where and how new interaction techniques can be integrated into the car.

Key Contributions

Workshop organization and community building

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Citation

@inproceedings{pflegling2012autonui, title={AutoNUI: 2nd Workshop on Automotive Natural User Interfaces}, author={Pflegling, Bastian and D{"o}ring, Tanja and Alvarez, Ignacio and Kranz, Matthias and Weinberg, Garrett and Healey, Jennifer}, booktitle={International Conference on Automotive User Interfaces and Interactive Vehicular Applications: 17/10/2012-19/10/2012}, year={2012} }

Pflegling, Bastian and D{"o (2012). AutoNUI: 2nd Workshop on Automotive Natural User Interfaces. International Conference on Automotive User Interfaces and Interactive Vehicular Applications: 17/10/2012-19/10/2012.

2011

iHelp, the Ubiquitous Vehicle User Help

Published in Proceedings of Interact 2011, 2011

Abstract

Voice-interfaced, in-vehicle assistance includes receiving a Voice-based query from a user in the vehicle, and then determining at least one of a user emotional state, user expertise level and speech recognition confidence level associated with the Voice-based query. A text-based query may then be derived from the Voice-based query, and used to search a help database for answers corresponding to the Voice-based query. At least one response is then provided to the user in the form of Voice-based assistance in accordance with at least one of the user emotional state, user expertise level and speech recognition confidence level.

Key Contributions

Voice-interfaced user help
Conversational dialogue management
Prototype development

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Citation

@inproceedings{alvarez2011ihelp, title={iHelp, the Ubiquitous Vehicle User Help}, author={Alvarez, Ignacio and Fischer, Hans-Peter}, booktitle={Proceedings of Interact}, pages={5--9}, year={2011} }

Alvarez, I. (2011). . Proceedings of Interact.

Designing driver centric natural voice user interfaces

Published in Adjunct Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 2011

Abstract

The current growth of automotive electronics aims toextend vehicle functionality and information access. Thispaper explores the application of Natural Voice UserInterfaces as a preferred interaction modality with in-vehicle technologies to lower driver distraction effects andimprove the user experience. The benefits and risks ofnatural speech interactions are evaluated in order topropose a driver-centric design guideline based on previousresearch. The paper concludes that driving scenarios canprofit considerably from systems that apply natural speechinterfaces to allow the driver to access information.

Key Contributions

Natural Voice Interaction
Voice-interfaced user help system design
Conversational Dialogue Managment
Context-aware system and architecture vision

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Citation

@inproceedings{alvarez2011designing, title={Designing driver-centric natural voice user interfaces}, author={Alvarez, Ignacio and Martin, Aqueasha and Dunbar, Jerone and Taiber, Joachim and Wilson, Dale-Marie and Gilbert, Juan E}, booktitle={Adjunct Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications. online}, pages={156--159}, year={2011} }

Alvarez, Ignacio and Martin, Aqueasha and Dunbar, Jerone and Taiber, Joachim and Wilson, Dale-Marie and Gilbert, Juan E (2011). Designing driver centric natural voice user interfaces. Adjunct Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications. online.

Autonui a workshop on automotive natural user interfaces

Published in ‘Proceedings of AutomotiveUI 2011’, ACM 2011

Abstract

Natural user interfaces by means of gesture and speech interaction have become a hot topic in research as well as already for real products. Most use cases currently center around consumer electronics devices like smart phones, TV sets, gaming, or other large screens like tabletops. Motivated by the latest results in those areas, our vision is to apply natural user interfaces like gesture and speech interaction to the automotive domain as well. This integration might on one hand reduce driver distraction in certain cases and on the other hand allow to design new experiences for operating infotainment and entertainment systems. The goal of this workshop is to explore the design space of natural multi-modal automotive user interfaces. We would like to analyze where and how new interaction techniques can be integrated into the car.

Key Contributions

Natural User Interfaces in Automotive
Workshop organization and community building

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Citation

@inproceedings{pfleging2011autonui, title={Autonui: a workshop on automotive natural user interfaces}, author={Pfleging, Bastian and Schmidt, Albrecht and D{"o}ring, Tanja and Knobel, Martin}, booktitle={Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages={207--209}, year={2011} }

Pfleging, B., Schmidt, A., Döring, T., & Knobel, M. (2011). Autonui: a workshop on automotive natural user interfaces. Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 207-209.

Are educational video games all they are cracked up to be

Published in ‘International Journal of Learning Technology’, 2011

Abstract

This paper investigates the benefits of learning from educational video games compared to learning by reading from a text document. The participants were exposed to Lewis and Clark expedition via a video game or text document. During the learning task, playing the game or reading, participants wore a Brain Computer Interface (BCI) device to gather their level of engagement. After the learning sessions, post-experiment questionnaires were used to assess the amount of information retained after each session. The results of this study suggests that the educational video games might not be significantly engaging, and also that learning by reading a handout may be better for retaining information. Furthermore, this paper briefly discusses the BCI device, and how it can be used to measure engagement of the participants.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

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Citation

@inproceedings{andujar2011educational, title={Are educational video games all they're cracked up to be?: A physiological approach for measuring engagement in educational video games vs. conventional learning techniques}, author={Andujar, Marvin and Ekandem, Josh and Alvarez, Ignacio and James, Melva and Gilbert, Juan}, booktitle={eLearn: World Conference on EdTech}, pages={539--544}, year={2011}, organization={Association for the Advancement of Computing in Education (AACE)} }

Andujar, M., Ekandem, J., Alvarez, I., James, M., & Gilbert, J. (2011). Are educational video games all they're cracked up to be?: A physiological approach for measuring engagement in educational video games vs. conventional learning techniques. eLearn: World Conference on EdTech, 539-544.

2010

Voice interfaced vehicle user help

Published in Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 2010

Abstract

Manuals were designed to provide support and information about the usage and maintenance of the vehicle. In many cases user’s manuals are the driver’s only guidance. However, lack of clarity and efficiency of manuals lead to user dissatisfaction. In vehicles this problem is even more crucial given that driving a motor vehicle is, for many people, the most complex and potentially dangerous task they will perform during their lifetime. In this paper we present a voice interfaced driver manual that can potentially fix the deficiencies of its alternatives. In addition we aim to provide a case for the integration of such technology in a vehicle to reduce driver distraction, increase driver satisfaction, and manual usability, while also benefiting Original Equipment Manufacturers (OEMs) in lowering costs and reducing the documentation process.

Key Contributions

Voice-interfaced user help system design
Conversational query methodology
Dynamic voice user interface architecture
Prototype development and database design
Context-aware system and architecture vision

Download paper

Citation

@article{ignacio2010voiceinterfaced, author = {Ignacio Alvarez et al.}, title = {Voice interfaced vehicle user help}, journal = {Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, year = {2010} }

Alvarez, I., et al. (2010). Voice interfaced vehicle user help. Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

Executive Portfolio

IGNACIO ALVAREZ

Technology Executive | Automotive & AI Leader

📍 Based in Germany · Open to Relocation Globally

ignacio.alvarez@thi.de LinkedIn

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Executive Summary

Technology Executive with a track record of CTO-level impact in the Automotive & AI sectors. Proven leader with 15+ years of experience architecting and scaling intelligent systems from R&D to series production, driving 30% YoY R&D ROI amidst budget reductions, leading global R&D organizations of 100+ engineers, and launching platforms adopted by 1M+ users. Expert in defining technical vision that aligns with C-suite objectives, secures market leadership, and generates massive IP value (150+ patents).

Core Competencies

Global R&D Leadership (100+ engineers) Technology Strategy & Roadmaps Productization & Tech Transfer Automated Driving Architecture Compute Platform Strategy Artificial Intelligence & Machine Learning Deep Learning & Generative AI Functional Safety Connected Vehicles (V2X) User-Centered Design Human–Machine Interface (HMI) Cyber-Physical Security Digital Twins & Large Scale Simulation Edge AI & Perception Stacks Behavior/Planning Verification & Validation Connected Vehicle Services Standards Leadership & Ecosystem Patent Strategy & Portfolio Development Cross-Functional Leadership Fluent in English, Spanish & German Basic Mandarin

IEEE Intelligent Transportation Systems Society – Member of Board of Governors (elected role driving global ITS strategy)

ACM AutomotiveUI Conference – Steering Committee Member (shaping annual agenda for automotive UX innovation)

Editorial Boards – Board Member for IEEE ITS Magazine and Int. Journal of Human-Computer Interaction

Senior Member, IEEE and Member, ACM, SAE (recognized professional standing and contributions in tech)

Thought Leadership & Innovation

Patents

Holder of 150+ patents (50+ granted) in autonomous systems, AI, and robotics, shaping the core IP strategy for market-leading mobility solutions.

View Patent Portfolio

Publications

Author of 70+ publications in top-tier IEEE and ACM venues and editor of multiple books on autonomous system engineering.

View Publications

Industry Standards

Key contributor to the definition of global safety and communication standards for autonomous vehicles through leadership roles in IEEE and ETSI.

View Standards Work

Education

Ph.D. in Computer Science (Automotive Intelligent Systems)

University of the Basque Country, Spain & Clemson University, USA, 2012

Summa Cum Laude

Joint international doctoral program focusing on conversational AI for vehicles.

M.Sc. in Media and Information Technology

Hochschule Offenburg, Germany, 2009

Coursework in multimedia systems and human-computer interaction.

B.Sc. in Communication Sciences

University of Burgos, Spain, 2006

Summa Cum Laude

Minor in Telecommunications.

Sitemap

{% include base_path %} A list of all the posts and pages found on the site. For you robots out there is an [XML version]({{ base_path }}/sitemap.xml) available for digesting as well.

Pages

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Posts

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Talk map

This map is generated from a Jupyter Notebook file in /_talks/talkmap.ipynb, which mines the location fields in the .md files in _talks/.

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See a map of all the places I've given a talk!

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Talks

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Blog posts

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Jupyter notebook markdown generator

# Jupyter notebook markdown generator These .ipynb files are Jupyter notebook files that convert a TSV containing structured data about talks (`talks.tsv`) or presentations (`presentations.tsv`) into individual markdown files that will be properly formatted for the academicpages template. The notebooks contain a lot of documentation about the process. The .py files are pure python that do the same things if they are executed in a terminal, they just don't have pretty documentation.

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Sitemap: {{ "sitemap.xml" | absolute_url }}

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You can have many headings

No venue listed (2024)

PDF

BibTeX

@article{liuyang2024misbehaviordete,
  author    = {Liuyang Yang et al.},
  title     = {Misbehavior detection using data consistency checks for collective perception messages},
  journal  = {No venue listed},
  year      = {2024}
}

Portfolio item number 1

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Voice interfaced vehicle user help

Published in Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 2010

Abstract

Manuals were designed to provide support and information about the usage and maintenance of the vehicle. In many cases user’s manuals are the driver’s only guidance. However, lack of clarity and efficiency of manuals lead to user dissatisfaction. In vehicles this problem is even more crucial given that driving a motor vehicle is, for many people, the most complex and potentially dangerous task they will perform during their lifetime. In this paper we present a voice interfaced driver manual that can potentially fix the deficiencies of its alternatives. In addition we aim to provide a case for the integration of such technology in a vehicle to reduce driver distraction, increase driver satisfaction, and manual usability, while also benefiting Original Equipment Manufacturers (OEMs) in lowering costs and reducing the documentation process.

Key Contributions

Voice-interfaced user help system design
Conversational query methodology
Dynamic voice user interface architecture
Prototype development and database design
Context-aware system and architecture vision

Download paper

Citation

@article{ignacio2010voiceinterfaced, author = {Ignacio Alvarez et al.}, title = {Voice interfaced vehicle user help}, journal = {Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, year = {2010} }

Alvarez, I., et al. (2010). Voice interfaced vehicle user help. Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

Are educational video games all they are cracked up to be

Published in ‘International Journal of Learning Technology’, 2011

Abstract

This paper investigates the benefits of learning from educational video games compared to learning by reading from a text document. The participants were exposed to Lewis and Clark expedition via a video game or text document. During the learning task, playing the game or reading, participants wore a Brain Computer Interface (BCI) device to gather their level of engagement. After the learning sessions, post-experiment questionnaires were used to assess the amount of information retained after each session. The results of this study suggests that the educational video games might not be significantly engaging, and also that learning by reading a handout may be better for retaining information. Furthermore, this paper briefly discusses the BCI device, and how it can be used to measure engagement of the participants.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

Download paper

Citation

@inproceedings{andujar2011educational, title={Are educational video games all they're cracked up to be?: A physiological approach for measuring engagement in educational video games vs. conventional learning techniques}, author={Andujar, Marvin and Ekandem, Josh and Alvarez, Ignacio and James, Melva and Gilbert, Juan}, booktitle={eLearn: World Conference on EdTech}, pages={539--544}, year={2011}, organization={Association for the Advancement of Computing in Education (AACE)} }

Andujar, M., Ekandem, J., Alvarez, I., James, M., & Gilbert, J. (2011). Are educational video games all they're cracked up to be?: A physiological approach for measuring engagement in educational video games vs. conventional learning techniques. eLearn: World Conference on EdTech, 539-544.

Autonui a workshop on automotive natural user interfaces

Published in ‘Proceedings of AutomotiveUI 2011’, ACM 2011

Abstract

Natural user interfaces by means of gesture and speech interaction have become a hot topic in research as well as already for real products. Most use cases currently center around consumer electronics devices like smart phones, TV sets, gaming, or other large screens like tabletops. Motivated by the latest results in those areas, our vision is to apply natural user interfaces like gesture and speech interaction to the automotive domain as well. This integration might on one hand reduce driver distraction in certain cases and on the other hand allow to design new experiences for operating infotainment and entertainment systems. The goal of this workshop is to explore the design space of natural multi-modal automotive user interfaces. We would like to analyze where and how new interaction techniques can be integrated into the car.

Key Contributions

Natural User Interfaces in Automotive
Workshop organization and community building

Download paper

Citation

@inproceedings{pfleging2011autonui, title={Autonui: a workshop on automotive natural user interfaces}, author={Pfleging, Bastian and Schmidt, Albrecht and D{"o}ring, Tanja and Knobel, Martin}, booktitle={Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages={207--209}, year={2011} }

Pfleging, B., Schmidt, A., Döring, T., & Knobel, M. (2011). Autonui: a workshop on automotive natural user interfaces. Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 207-209.

Designing driver centric natural voice user interfaces

Published in Adjunct Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 2011

Abstract

The current growth of automotive electronics aims toextend vehicle functionality and information access. Thispaper explores the application of Natural Voice UserInterfaces as a preferred interaction modality with in-vehicle technologies to lower driver distraction effects andimprove the user experience. The benefits and risks ofnatural speech interactions are evaluated in order topropose a driver-centric design guideline based on previousresearch. The paper concludes that driving scenarios canprofit considerably from systems that apply natural speechinterfaces to allow the driver to access information.

Key Contributions

Natural Voice Interaction
Voice-interfaced user help system design
Conversational Dialogue Managment
Context-aware system and architecture vision

Download paper

Citation

@inproceedings{alvarez2011designing, title={Designing driver-centric natural voice user interfaces}, author={Alvarez, Ignacio and Martin, Aqueasha and Dunbar, Jerone and Taiber, Joachim and Wilson, Dale-Marie and Gilbert, Juan E}, booktitle={Adjunct Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications. online}, pages={156--159}, year={2011} }

Alvarez, Ignacio and Martin, Aqueasha and Dunbar, Jerone and Taiber, Joachim and Wilson, Dale-Marie and Gilbert, Juan E (2011). Designing driver centric natural voice user interfaces. Adjunct Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications. online.

iHelp, the Ubiquitous Vehicle User Help

Published in Proceedings of Interact 2011, 2011

Abstract

Voice-interfaced, in-vehicle assistance includes receiving a Voice-based query from a user in the vehicle, and then determining at least one of a user emotional state, user expertise level and speech recognition confidence level associated with the Voice-based query. A text-based query may then be derived from the Voice-based query, and used to search a help database for answers corresponding to the Voice-based query. At least one response is then provided to the user in the form of Voice-based assistance in accordance with at least one of the user emotional state, user expertise level and speech recognition confidence level.

Key Contributions

Voice-interfaced user help
Conversational dialogue management
Prototype development

Download paper

Citation

@inproceedings{alvarez2011ihelp, title={iHelp, the Ubiquitous Vehicle User Help}, author={Alvarez, Ignacio and Fischer, Hans-Peter}, booktitle={Proceedings of Interact}, pages={5--9}, year={2011} }

Alvarez, I. (2011). . Proceedings of Interact.

AutoNUI: 2nd Workshop on Automotive Natural User Interfaces

Published in ‘Adjunct Proceedings of the 4th International Conference on Automotive User Interfaces, ACM 2012

Abstract

Natural user interfaces—generally based on gesture and speech interaction—are an increasingly hot topic in research and are already being applied in a multitude of commercial products. Most use cases currently involve consumer electronics devices like smart phones, tablets, TV sets, game consoles, or large-screen tabletop computers.Motivated by the latest results in those areas, our vision is to apply natural user interfaces, for example gesture and conversational speech interaction, to the automotive domain as well. This integration might on one hand reduce driver distraction in certain cases and on the other hand might allow the design of new user experiences for infotainment and entertainment systems. The goal of this workshop is to explore the design space of natural multi-modal automotive user interfaces and to continue the fruitful discussions held at the 1st Workshop on Automotive Natural User Interfaces from AutomotiveUI ’11 in Salzburg, Austria. We would like to analyze where and how new interaction techniques can be integrated into the car.

Key Contributions

Workshop organization and community building

Download paper

Citation

@inproceedings{pflegling2012autonui, title={AutoNUI: 2nd Workshop on Automotive Natural User Interfaces}, author={Pflegling, Bastian and D{"o}ring, Tanja and Alvarez, Ignacio and Kranz, Matthias and Weinberg, Garrett and Healey, Jennifer}, booktitle={International Conference on Automotive User Interfaces and Interactive Vehicular Applications: 17/10/2012-19/10/2012}, year={2012} }

Pflegling, Bastian and D{"o (2012). AutoNUI: 2nd Workshop on Automotive Natural User Interfaces. International Conference on Automotive User Interfaces and Interactive Vehicular Applications: 17/10/2012-19/10/2012.

Contribution to the development of intelligent conversational assistants in automotive environments

Published in EHU Scientific Production Portal, EHU 2012

Abstract

This thesis presents the design and development of the Conversational Automotive Assistant, Voice User Help, a conversational system based on the question-answer paradigm and designed to consult vehicle documentation while driving. This work compiles research in the fields of technical documentation, information retrieval, natural language processing, vehicle user interface design, user experience, and affective computing with the goal of creating an adaptive and dynamic assistant that modifies its conversational behavior depending on the user’s emotional state.

Key Contributions

Conversational Asistant for In-cabin Automotive Applications
Information Retrieval Algorithms
Human-Computer Interaction Voice Interfaces
Automotive Context-aware Affective Computing

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Citation

@phdthesis{martinez2012contribution, title={Contribution to the development of intelligent conversational assistants in automotive environments}, author={Mart{\'\i}nez, Ignacio Javier {\'A}lvarez}, year={2012}, school={Universidad del Pa{\'\i}s Vasco-Euskal Herriko Unibertsitatea} }

Alvarez, I. (2012). . .

Emotional Adaptive Vehicle User Interfaces, Moderating negative effects of failed technology interactions while driving

Published in ‘Adjunct Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI 2012)’, 2012

Abstract

Automotive Natural User Interfaces have the potential to increase user experience providing intuitive interactions for drivers. However, in the complex setting of a driving vehicle, failed interactions with in-vehicle technology can lead to frustration and put drivers in a dangerous situation. This paper evaluates the possibility of applying emotion recognition to vehicular spoken dialogue systems in order to adapt the dialog strategies, in error recovery scenarios. An emotional taxonomy is developed for the interactions with a conversational vehicular application, the Voice User Help. The positive results of the performance of VUH emotion recognizer support the creation of real-time classification of the user emotional state, which serves as basis to emotional reappraisal dialog strategies that mitigate negative effects on the driver’s cognitive load and driver performance.

Key Contributions

Developed the Voice User Help system
Created a VUH-specific emotional taxonomy
Built a real-time emotion recognizer
Proposed adaptive emotional dialog strategies
Used emotion for error recovery

Download paper

Citation

@inproceedings{alvarez2012emotional, title={Emotional Adaptive Vehicle User Interfaces: moderating negative effects of failed technology interactions while driving}, author={Alvarez, Ignacio and Lopez-de Ipi{\~n}a, Karmele and Daily, Shaundra B and Gilbert, Juan E}, booktitle={Proceedings of Workshop of Automotive Natural Interfaces, together with International Conference on Automotive User Interfaces}, pages={57--60}, year={2012} }

Alvarez, Ignacio and Lopez-de Ipi{\~n (2012). Emotional Adaptive Vehicle User Interfaces, Moderating negative effects of failed technology interactions while driving. Proceedings of Workshop of Automotive Natural Interfaces, together with International Conference on Automotive User Interfaces.

Evaluating the ergonomics of BCI devices for research and experimentation

Published in Journal of Ergonomics, Taylor & Francis Online 2012

Abstract

The use of brain computer interface (BCI) devices in research and applications has exploded in recent years. Applications such as lie detectors that use functional magnetic resonance imaging (fMRI) to video games controlled using electroencephalography (EEG) are currently in use. These developments, coupled with the emergence of inexpensive commercial BCI headsets, such as the Emotiv EPOC ( http://emotiv.com/index.php) and the Neurosky MindWave, have also highlighted the need of performing basic ergonomics research since such devices have usability issues, such as comfort during prolonged use, and reduced performance for individuals with common physical attributes, such as long or coarse hair. This paper examines the feasibility of using consumer BCIs in scientific research. In particular, we compare user comfort, experiment preparation time, signal reliability and ease of use in light of individual differences among subjects for two commercially available hardware devices, the Emotiv EPOC and the Neurosky MindWave. Based on these results, we suggest some basic considerations for selecting a commercial BCI for research and experimentation.

Statement of Relevance: Despite increased usage, few studies have examined the usability of commercial BCI hardware. This study assesses usability and experimentation factors of two commercial BCI models, for the purpose of creating basic guidelines for increased usability. Finding that more sensors can be less comfortable and accurate than devices with fewer sensors.

Key Contributions

BCI evaluation
BCI Signal Limitation Modelling
Practical Use recommendations

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Citation

@article{ekandem2012evaluating, title={Evaluating the ergonomics of BCI devices for research and experimentation}, author={Ekandem, Joshua I and Davis, Timothy A and Alvarez, Ignacio and James, Melva T and Gilbert, Juan E}, journal={Ergonomics}, volume={55}, number={5}, pages={592--598}, year={2012}, publisher={Taylor \& Francis} }

Alvarez, I. (2012). . Ergonomics.

The voice user help, a smart vehicle assistant for the elderly

Published in Proceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence, Springer 2012

Abstract

The rapid advancement of vehicular technologies has resulted in an exponential increase of new vehicle functions road assistance and connected vehicles services. Vehicle manuals are designed to provide support and information about the use and maintenance of these features. However, current vehicle user manuals do not allow looking for information under driving conditions. This paper presents the Voice User Help, a smart voice-operated system that utilizes natural language understanding and emotional adaptive interfaces to assist drivers when looking for vehicle information with minimal effect on their driving performance. Additionally, the system presents an opportunity for elder drivers to reduce the learning curve of new in-vehicle technologies and improve efficiency. Results on user acceptance of the Voice User Help, as well as cognitive load and driver distraction effects generated during a simulated drive indicate that the Voice User help is an extremely desirable feature and potentially safe application since it did not significantly decrement driving performance. Furthermore preliminary results on adaptive voice interfaces using emotion recognition indicate that personalization of the interaction will be able to palliate possible negative effects that happen during system error recovery.

Key Contributions

Developed voice-operated vehicle help system
Aimed to assist elderly drivers
Evaluated driver distraction and usability
Integrated emotion recognition for adaptation
Created a VUH-specific emotional taxonomy

Download paper

BibTeX

Driver in the loop: Best practices in automotive sensing and feedback mechanisms

Published in ‘Automotive user interfaces: creating interactive experiences in the car’, 2017

Abstract

Given the rapid advancement of technologies in the automotive domain, driver–vehicle interaction has recently become more and more complicated. The amount of research applied to the vehicle cockpit is increasing, with the advent of (highly) automated driving, as the range of interaction that is possible in a driving vehicle expands. However, as opportunities increase, so does the number of challenges that automotive user experience designers and researchers will face. This chapter focuses on the instrumentation of sensing and displaying techniques and technologies to make better user experience while driving. In the driver–vehicle interaction loop, the vehicle can sense driver states, analyze, estimate, and model the data, and then display it through the appropriate channels for intervention purposes. To improve the interaction, a huge number of new/affordable sensing (EEG, fNIRS, IR imaging) and feedback (head-up displays, auditory feedback, tactile arrays, etc.) techniques have been introduced. However, little research has attempted to investigate this area in a systematic way. This chapter provides an overview of recent advances of input and output modalities to be used for timely, appropriate driver–vehicle interaction. After outlining relevant background, we provide information on the best-known practices for input and output modalities based on the exchange results from the workshop on practical experiences for measuring and modeling drivers and driver–vehicle interactions at AutomotiveUI 2015. This chapter can help answer research questions on how to instrument a driving simulator or realistic study to gather data and how to place interaction outputs to enable appropriate driver interactions.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

Download paper

Citation

@incollection{riener2017driver, title={Driver in the loop: Best practices in automotive sensing and feedback mechanisms}, author={Riener, Andreas and Jeon, Myounghoon and Alvarez, Ignacio and Frison, Anna K}, booktitle={Automotive user interfaces: creating interactive experiences in the car}, pages={295--323}, year={2017}, publisher={Springer} }

Riener, Andreas and Jeon, Myounghoon and Alvarez, Ignacio and Frison, Anna K (2017). Driver in the loop: Best practices in automotive sensing and feedback mechanisms. Automotive user interfaces: creating interactive experiences in the car.

Skyline: A Platform Towards Scalable UX-Centric In-Vehicle HMI Development

Published in ‘International Journal of Mobile Human Computer Interaction (IJMHCI)’, 2017

Abstract

This paper describes the research and development process of an in-vehicle user experience using Skyline, an automotive prototyping platform created in Intel Labs to empower interaction designers and user experience researches to rapidly and iteratively develop and test in-vehicle user experience concepts. The paper describes the hardware and software components of Skyline in depth and how to configure them to suit individual researcher needs. The paper also presents a case study to exemplify the design making process that Skyline enables. From ideation to use-case creation, prototyping and validation through user assessment, the paper showcases the benefits of capturing early qualitative user feedback as support for rapid prototyping walking through a study titled Agency vs. Control and the associated interactions inside the cockpit. Ten defined use-cases are developed and integrated into a hero scenario in Skyline. High fidelity HMI concepts are tested and validated over the course of six months with feedback from a total of fifty users.

Key Contributions

Automotive HMI Prototyping
Rapid HCI User Interface Design
Evaluation Methods

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Citation

@article{alvarez2017skyline, title={Skyline: A Platform Towards Scalable UX-Centric In-Vehicle HMI Development}, author={Alvarez, Ignacio and Rumbel, Laura}, journal={International Journal of Mobile Human Computer Interaction (IJMHCI)}, volume={9}, number={3}, pages={34--53}, year={2017}, publisher={IGI Global Scientific Publishing} }

Alvarez, Ignacio and Rumbel, Laura (2017). Skyline: A Platform Towards Scalable UX-Centric In-Vehicle HMI Development. International Journal of Mobile Human Computer Interaction (IJMHCI).

Socializing under the influence of distracted driving: a study of the effects of in-vehicle and outside-of-the-vehicle communication while driving

Published in ‘Springer Nature Advances in Human Aspects of Transportation’, Springer 2017

Abstract

Advancements of in-vehicle technologies and the development of mobile applications that keep a driver connected in a driving environment have caused an increasingly dangerous safety concern. Distracted driving has gained the attention of legislators and governments globally. Countries have constituted bans that partially or fully forbid drivers from using gadgets while driving, especially hindering out-of-the-vehicle communications. This paper introduces Voiceing™, a voice-activated application meant to improve social communications in the car, serving as a safe alternative to distracted driving. Other modalities of interaction such as texting, in-vehicle conversations and outside-of-the-vehicle conversation have been measured and compared with Voiceing™ investigating effects on driver’s performance, cognitive load and user acceptance. Results from this study suggest that Voiceing™ is a safer alternative than in-vehicle interactions with humans. Results also show that natural speech interaction of in-vehicle applications and the inclusion of context awareness help improve driving performance while interacting with a vehicle system.

Key Contributions

Introduces Voiceing™ a voice-activated application
Compares various in-car communication methods
Measures performance, cognitive load, acceptance
Voiceing™ is safer than human interactions
Natural speech improves driving performance

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Citation

@inproceedings{alnizami2016socializing, title={Socializing under the influence of distracted driving: a study of the effects of in-vehicle and outside-of-the-vehicle communication while driving}, author={Alnizami, Hanan and Alvarez, Ignacio and Gilbert, Juan E}, booktitle={Advances in Human Aspects of Transportation: Proceedings of the AHFE 2016 International Conference on Human Factors in Transportation, July 27-31, 2016, Walt Disney World{\textregistered}, Florida, USA}, pages={243--255}, year={2016}, organization={Springer} }

Alnizami, Hanan and Alvarez, Ignacio and Gilbert, Juan E (2016). Socializing under the influence of distracted driving: a study of the effects of in-vehicle and outside-of-the-vehicle communication while driving. Advances in Human Aspects of Transportation: Proceedings of the AHFE 2016 International Conference on Human Factors in Transportation, July 27-31, 2016, Walt Disney World{\textregistered.

The insight–prototype–product cycle best practices and processes to iteratively advance in-vehicle interactive experiences development

Published in Automotive User Interfaces: Creating Interactive Experiences in the Car, Springer Book, Springer 2017

Abstract

In-vehicle experiences are made up mainly of mundane small moments, repeated practices, and taken-for-granted decisions that make up daily experiences in and around private passenger vehicles. Understanding what those experiences are for drivers around the world presents an opportunity for designing novel interactive experiences, technologies, and user interfaces for vehicles. In this chapter, we present a set of tools, methodologies, and practices that will help reader create a holistic design space for future mobility. Transitioning between ethnography, insights, prototyping, experience design, and requirements decomposition is a challenging task even for experienced UX professionals. This chapter provides guidance in this matter with practical examples.

Key Contributions

Ethnographic research methods for Automotive System Development
Rapid prototyping methods for Automotive Functions
Experience Design mapping to product specifications
Product Development evaluation and validation
Product Success metrics

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Citation

@incollection{alvarez2017insight, title={The insight--prototype--product cycle best practices and processes to iteratively advance in-vehicle interactive experiences development}, author={Alvarez, Ignacio and Jordan, Adam and Knopf, Juliana and LeBlanc, Darrell and Rumbel, Laura and Zafiroglu, Alexandra}, booktitle={Automotive User Interfaces: Creating Interactive Experiences in the Car}, pages={377--400}, year={2017}, publisher={Springer} }

Alvarez, I. (2017). . Automotive User Interfaces: Creating Interactive Experiences in the Car.

Towards Adaptive Ambient In-Vehicle Displays and Interactions: Insights and Design Guidelines from the 2015 AutomotiveUI Dedicated Workshop

Published in ‘Automotive User Interfaces: Creating Interactive Experiences in the Car’, ACM 2017

Abstract

Informing a driver of a vehicle’s changing state and environment is a major challenge that grows with the introduction of in-vehicle assistant and infotainment systems. Even in the age of automation, the human will need to be in the loop for monitoring, taking over control, or making decisions. In these cases, poorly designed systems could lead to needless attentional demands imparted on the driver, taking it away from the primary driving task. Existing systems are offering simple and often unspecific alerts, leaving the human with the demanding task of identifying, localizing, and understanding the problem. Ideally, such systems should communicate information in a way that conveys its relevance and urgency. Specifically, information useful to promote driver safety should be conveyed as effective calls for action, while information not pertaining to safety (therefore less important) should be conveyed in ways that do not jeopardize driver attention. Adaptive ambient displays and peripheral interactions have the potential to provide superior solutions and could serve to unobtrusively present information, to shift the driver’s attention according to changing task demands, or enable a driver to react without losing the focus on the primary task. In order to build a common understanding across researchers and practitioners from different fields, we held a Workshop on Adaptive Ambient In-Vehicle Displays and Interactions at the AutomotiveUI`15 conference. In this chapter, we discuss the outcomes of this workshop, provide examples of possible applications now or in the future and conclude with challenges in developing or using adaptive ambient interactions.

Key Contributions

Workshop organization and community building
Human-computer interaction design

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Citation

@Inbook{Löcken2017, author={Löcken, Andreas and Sadeghian Borojeni, Shadan and Müller, Heiko and Gable, Thomas M. and Triberti, Stefano and Diels, Cyriel and Glatz, Christiane and Alvarez, Ignacio and Chuang, Lewis and Boll, Susanne}, editor={Meixner, Gerrit and Müller, Christian}, title={Towards Adaptive Ambient In-Vehicle Displays and Interactions: Insights and Design Guidelines from the 2015 AutomotiveUI Dedicated Workshop}, booktitle={Automotive User Interfaces: Creating Interactive Experiences in the Car}, year={2017}, publisher={Springer International Publishing}, address={Cham}, pages={325--348}, isbn={978-3-319-49448-7}, doi={10.1007/978-3-319-49448-7_12}, url={https://doi.org/10.1007/978-3-319-49448-7_12} }

Löcken, A., Sadeghian Borojeni, S., Müller, H., Gable, T. M., Triberti, S., Diels, C., Glatz, C., Alvarez, I., Chuang, L., & Boll, S. (2017). Towards Adaptive Ambient In-Vehicle Displays and Interactions: Insights and Design Guidelines from the 2015 AutomotiveUI Dedicated Workshop. In G. Meixner & C. Müller (Eds.), Automotive User Interfaces: Creating Interactive Experiences in the Car (pp. 325-348). Springer International Publishing.

Workshop on user-centered design for automated driving systems

Published in Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, ACM 2017

Abstract

Automated driving systems (ADS) are mainly regarded from an innovation and technology-centered perspective. In academia, as well as in industry, there is a concentration on technical issues to maintain competitiveness while aspects like acceptance, trust and user experience are widely under-researched. However, the “human factor” is critical for a comprehensive establishment of ADS technology on the market. We believe that there is a need to focus on a user-centered design (UCD) perspective to bring ADS innovation to a next level and to achieve a wide acceptance in society. In this workshop we want to discuss special requirements of UCD applied to ADS, to address challenges and opportunities and to reveal new research fields for future work.

Key Contributions

Proposes migration-tolerant ATC display concept
Advocates for stepwise functionality introduction
Compares small versus large integration steps
Measures usability, learnability, and acceptance

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Citation

@inproceedings{10.1145/3131726.3131734, author = {Frison, Anna-Katharina and Pfleging, Bastian and Riener, Andreas and Jeon, Myounghoon Philart and Alvarez, Ignacio and Ju, Wendy}, title = {Workshop on User-Centered Design for Automated Driving Systems}, year = {2017}, isbn = {9781450351515}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3131726.3131734}, doi = {10.1145/3131726.3131734}, booktitle = {Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications Adjunct}, pages = {22–27}, numpages = {6}, keywords = {User-centered design, Automotive user interfaces, Automated driving systems}, location = {Oldenburg, Germany}, series = {AutomotiveUI '17} }

Alvarez, I. (2017). . Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications Adjunct.

An international survey on automated and electric vehicles: Austria, Germany, South Korea, and USA

Published in ‘Springer Natural Digital Human Modelling’, Springer 2018

Abstract

As development of automated vehicles and adoption of electric vehicles continue to grow, there is an increasing interest in the public opinions on these technologies. We conducted an international online survey to gather information about people’s hopes and concerns for automated and electric vehicles from a total of 866 people from four countries – Austria, Germany, South Korea, and USA. Results revealed some differences across countries in the perceptions of automated and electric vehicles. However, differences between the same countries have shrunk compared to our previous survey completed in 2012. Results are discussed with limitations and future work.

Key Contributions

Automated driving system development
Electric Vehicles
International Trends

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Citation

@inproceedings{jeon2018international, title={An international survey on automated and electric vehicles: Austria, Germany, South Korea, and USA}, author={Jeon, Myounghoon and Riener, Andreas and Sterkenburg, Jason and Lee, Ju-Hwan and Walker, Bruce N and Alvarez, Ignacio}, booktitle={International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management}, pages={579--587}, year={2018}, organization={Springer} }

Jeon, Myounghoon and Riener, Andreas and Sterkenburg, Jason and Lee, Ju-Hwan and Walker, Bruce N and Alvarez, Ignacio (2018). An international survey on automated and electric vehicles: Austria, Germany, South Korea, and USA. International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management.

ARV 2018: 2nd Workshop on Augmented Reality for Intelligent Vehicles

Published in ‘Adjunct Proceedings of the 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’18)’, ACM 2018

Abstract

Augmented reality (AR) has the potential to improve road safety, support more immersive (non-) driving related activities, and finally enhance driving experience. AR may also be the enabling technology to help on the transition towards automated driving. However, augmented reality still faces a number of technical challenges when applied in vehicles, and also several human factors issues need to be solved. In this workshop, we will discuss potential and constraints as well as impact, role, and adequacy of AR in driving applications. The primary goal of this workshop is to define a research agenda for the use of AR in intelligent vehicles within the next 3 to 5 years.

Key Contributions

Workshop organization and community building
Defines a 3-5 year collaborative research agenda
Identifies four key AR problem fields
Discusses practical usage of AR devices
Outlines research challenges and hypotheses

In-car emotion detection and regulation have become an emerging and important branch of research within the automotive domain. Different emotional states can greatly influence human driving performance and user experience both in manual and automated driving conditions. The monitoring and regulation of relevant emotional states is therefore important to avoid critical driving scenarios with the human driver being in charge, and to ensure comfort and acceptance in autonomous driving. In this workshop we want to discuss the empathic user interface research to address challenges and opportunities and to reveal new research directions for future work. This workshop provides a forum for exchange and discussion on empathic user interfaces, including methods for emotion recognition and regulation, empathic automotive human-machine interaction design, user evaluation and measurements, and subsequent improvement of autonomous driving experience.

Key Contributions

Discusses empathic in-car user interfaces
Identifies critical scenarios and emotions
Explores emotion recognition and regulation methods
Uses 4mat system for workshop structure
Creates roadmap for future research

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Citation

@inproceedings{10.1145/3239092.3239098, author = {Bosch, Esther and Oehl, Michael and Jeon, Myounghoon and Alvarez, Ignacio and Healey, Jennifer and Ju, Wendy and Jallais, Christophe}, title = {Emotional GaRage: A Workshop on In-Car Emotion Recognition and Regulation}, year = {2018}, isbn = {9781450359474}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3239092.3239098}, doi = {10.1145/3239092.3239098}, booktitle = {Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages = {44–49}, numpages = {6}, keywords = {user acceptance, empathic vehicles, emotion recognition and regulation, Driver state assessment}, location = {Toronto, ON, Canada}, series = {AutomotiveUI '18} }

Bosch, Esther and Oehl, Michael and Jeon, Myounghoon and Alvarez, Ignacio and Healey, Jennifer and Ju, Wendy and Jallais, Christophe (2018). Emotional GaRage: A workshop on in-car emotion recognition and regulation. Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

“Peace of Mind”, An Experiential Safety Framework for Automated Driving Technology Interactions

Published in Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), IEEE 2018

Abstract

Current automated driving systems assume drivers continuously monitor the vehicle. Meanwhile, fully automated vehicles aim at not requiring human intervention for their safely operation. The industry is currently debating how these novel systems can be certified under functional safety standards. In this paper, we argue that the current safety picture is not comprehensive enough, since it alienates users. We propose experiential safety as a complement to existing functional safety and to develop a framework for experiential safety interactions between the user and automation in automated driving environments. To support the experiential safety design model, we provide an overview of the user-centered research on experiential automation safety, which includes results from online surveys, personal interviews, and gamified group workshops. We explore current user behaviors by focusing on what makes them feel safe as drivers and passengers, and how unexpected events and automation responses might impact their perception of safety. Among the highlighted results, we show how mismatched expectations and unexpected behaviors from autonomous vehicles can lead to frustration and compromised trust. We also show how automation feedback to the user can generate stress and anxiety if not properly configured and how a cooperative relationship between automation and the driver leads to more satisfying driving experiences. Finally, we present guidelines for the experiential safety to be applied by automotive engineers and designers in their development of automated driving technologies.

Key Contributions

Proposes an experiential safety concept for AVs
Develops a novel gamified workshop method
Presents an experiential safety framework
Defines user-lenses and machine-lenses guidelines
Finds collaborative feedback improves user emotion

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Citation

@INPROCEEDINGS{8569686, author={Strano, Marina and Novak, Fabricio and Walbert, Shelly and Palmeiro, Beatriz and Morales, Sonia and Alvarez, Ignacio}, booktitle={2018 21st International Conference on Intelligent Transportation Systems (ITSC)}, title={“Peace of Mind”, An Experiential Safety Framework for Automated Driving Technology Interactions}, year={2018}, volume={}, number={}, pages={53-59}, keywords={Safety;Automation;Interviews;Automobiles;Guidelines;Task analysis;Automated Driving;Experiential Safety;User experience design;design research;design theory;design science;methodology design;emerging technology}, doi={10.1109/ITSC.2018.8569686} }

Alvarez, I. (2018). . 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

Theo, take a right… uh… left: Conversational Route Negotiations with Autonomous Driving Assistants

Published in Adjunct Proceedings of the 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’18), ACM 2018

Abstract

We foresee conversational driver assistants playing a crucial role in automated driving interactions. In this video we present a study of user interactions with an in-vehicle agent, “Theo”, under SAE Level 4 automated driving. We use a remote Wizard-of-Oz setup where participants, sitting in a driving simulator, experience real-life video footage transmitted from a vehicle in the neighborhood and interact with Theo to instruct the vehicle where to go. We configured Theo to present 3 levels of conversational abilities (terse, verbose and helpful). We show the results of 9 participants tasked to negotiate destinations and route changes. Voice interaction was reported as preferred means of communication with Theo. There was a clear preference for talkative assistants which were perceived more responsive and intelligent. We highlight challenging interactions for users such as vehicle maneuvers in parking areas and specifying drop off points and interesting associations between the agent performance and the automated vehicle abilities.

Key Contributions

Automated driving system development
Pioneers a remote AI Wizard-of-Oz setup
Tests three conversational agent styles
Finds user preference for talkative agents
Identifies challenging user interaction scenarios
Links agent performance to perceived safety

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Citation

@inproceedings{10.1145/3239092.3267414, author = {Ekandem, Joshua E. and Alvarez, Ignacio and Rayburn, Cat and Johnson, Andrea}, title = {Theo, take a right ... uh ... left: Conversational Route Negotiations with Autonomous Driving Assistants}, year = {2018}, isbn = {9781450359474}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3239092.3267414}, doi = {10.1145/3239092.3267414}, booktitle = {Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages = {261–262}, numpages = {2}, keywords = {Advanced Driver Assistance Systems, Automated Driving, Conversational Dialogues, Natural Language Understanding, Route-Based Negotiation}, location = {Toronto, ON, Canada}, series = {AutomotiveUI '18} }

Ekandem, Joshua E. and Alvarez, Ignacio and Rayburn, Cat and Johnson, Andrea (2018). Theo, take a right... uh... left: Conversational Route Negotiations with Autonomous Driving Assistants. Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

Towards understanding emotional reactions of driver-passenger dyads in automated driving

Published in Proceedings of the 13th IEEE International Conference on Automatic Face & Gesture Recognition, IEEE 2018

Abstract

Automated driving has the potential to reduce the amount of fatal crashes, lighten the burden of commutes, and democratize mobility access to wider populations. But delegation of control to automation is not without issues. One of the foreseen drawbacks is that users might experience negative emotional reactions to unanticipated or unexplainable automated maneuvers. In this paper we present a novel method to induce targeted emotional reactions, frustration and startle, in simulated automated driving environments. We describe the data collection process for 17 driver - passenger dyads and discuss the data labelling method for generating reliable novel emotion datasets. This contribution is a foundational methodology towards expanding emotional understanding in automated vehicles, a critical skill for building long-term trusted experiences.

Key Contributions

Presents method to induce driver emotions
Studies emotional reactions of driver-passenger dyads
Develops a reliable emotion labeling process
Achieves expert consensus on emotion labels
Provides methodology for emotional understanding

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Citation

@INPROCEEDINGS{8373886, author={Alyuz, Nese and Aslan, Sinem and Healey, Jennifer and Alvarez, Ignacio J. and Esme, Asli Arslan}, booktitle={2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)}, title={Towards Understanding Emotional Reactions of Driver-Passenger Dyads in Automated Driving}, year={2018}, volume={}, number={}, pages={585-592}, keywords={Labeling;Vehicles;Data collection;Cameras;Automation;Roads;affective computing;automated driving;drive simulation;labeling;inter-rater agreement}, doi={10.1109/FG.2018.00093} }

Alvarez, I. (2018). . 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018).

Workshop on designing highly automated driving systems as radical innovation

Published in Adjunct Proceedings of the 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’18), ACM 2018

Abstract

Automated driving systems (ADS), especially in higher levels of automation, seem to be the new focus of innovation regarding future mobility. Technological achievements of traveling automation open up new challenges for road traffic. Existing automotive research focuses on problem solving and observational approaches including users and their imagination of the future of mobility to analyze acceptance and user experience of “incremental” (step-wised improved) innovations. On the other hand, “radical” (something new, enabled by technology or meaning change) innovations extensively increase product quality leaping over incremental innovation. This workshop aims to challenge the current research approaches to automated driving against “trying to improve sitting in a horse carriage” and discuss how we can design “radical” innovations for ADS beyond the “horse carriage”. Within this interactive workshop, we will utilize a design thinking approach to refocus on underlying problems that ADSs originally aim to solve and generate ideas for radical innovations.

Key Contributions

Critiques incremental innovation in ADS research
Promotes designing ADS as radical innovation
Applies a design thinking approach
Utilizes rapid prototyping for radical ideas
Refocuses on problems ADS should solve

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Citation

@inproceedings{10.1145/3239092.3239097, author = {Frison, Anna-Katharina and Riener, Andreas and Jeon, Myounghoon and Pfleging, Bastian and Alvarez, Ignacio}, title = {Workshop on Designing Highly Automated Driving Systems as Radical Innovation}, year = {2018}, isbn = {9781450359474}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3239092.3239097}, doi = {10.1145/3239092.3239097}, booktitle = {Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages = {37–43}, numpages = {7}, keywords = {Automated Driving Systems, Design Thinking, Innovation, SAE J3016, User-Centered Design}, location = {Toronto, ON, Canada}, series = {AutomotiveUI '18} }

Alvarez, I. (2018). . Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

2nd special session on solving the automated vehicle safety assurance challenge

Published in Proceedings of the 2019 International Conference on Intelligent Transportation Systems (ITSC), IEEE 2019

Abstract

The scientific knowledge and tools for the mass deployment of Automated Vehicles (AVs) are maturing rapidly, as evidenced by the wide deployment of AV test fleets worldwide. These vehicles have the potential to produce tremendous economic and societal benefits including greatly reduced traffic accidents, injuries, and congestion, and to make less expensive, more flexible and more productive transportation available to all. But one challenge looms above all others in the race to full vehicle automation; solving the AV Safety Assurance challenge. Automated transportation is not just a product, but an industry. And it is as an industry that we must together solve this challenge. We invite researchers, automakers, technology companies, and government regulators to come together to develop a holistic model to define and measure AV Safety. In this special session we present papers that provide contributions to the definition, applicability and standardization of AV safety assurance, including methods for the development of metrics, benchmarks and evangelisation of AV Safety Assurances to users and the public. This session continues the open discussion started in 2018 ITSC with the goal of making AV Safety a reality. The session will consist of high quality paper presentations as well as a panel discussion with some of the most relevant figures of the AV industry.

Key Contributions

Common AV safety definition
Fosters collaboration between industry and academia
Seeks to specify AV safety standards
Gathers a universal view on safety

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Citation

@inproceedings{elli20192nd, title={2nd special session on solving the automated vehicle safety assurance challenge}, author={Elli, Maria Soledad and Alvarez, Ignacio and Ota, Jeffrey and Weast, Jack and Maurer, Markus}, booktitle={International Conference on Intelligent Transportation Systems (ITSC)}, year={2019} }

Alvarez, I. (2019). . International Conference on Intelligent Transportation Systems (ITSC).

Design of a misbehavior detection system for objects based shared perception V2X applications

Published in ‘IEEE Intelligent Transportation Systems Conference’, IEEE 2019

Abstract

The recent trend of integrating vehicular communications with advanced sensors installed on vehicles, enables Connected and Autonomous Vehicles (CAVs) to share their own driving information as well as perception information, such as a list of perceived objects (e.g., dynamic obstacles such as vehicles, pedestrians, and cyclists, and static obstacles). This has the potential to improve driving safety by expanding collective perception of vehicles. However, adversaries may also populate false information to other Connected Vehicles (CVs) via Vehicle-to-Vehicle (V2V) communications. This paper investigates the security aspects of mixed deployment of CAVs, CVs and legacy vehicles, and in particular with regards to misbehavior detection. We provide a generic design framework that is independent from the specific algorithms of the underlying perception system, and can be used to implement a practical Misbehavior Detection System (MDS). We analyze the MDS framework w.r.t. a ghost vehicle attack. While no computing system can be completely secure, we believe this work would help the industry to develop a practical MDS design within a common framework while allowing individual techniques to mature and evolve over time with future academic research.

Key Contributions

Proposes a generic misbehavior detection framework
Divides detection into local/collaborative components
Categorizes four levels of anomaly detection
Defines Unobservability and Undecidability concepts
Defines conditions for successful attacks

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Citation

@inproceedings{8917066, author={Ambrosin, Moreno and Yang, Lily L and Liu, Xiruo and Sastry, Manoj R and Alvarez, Ignacio J}, booktitle={2019 IEEE Intelligent Transportation Systems Conference (ITSC)}, title={Design of a Misbehavior Detection System for Objects Based Shared Perception V2X Applications}, year={2019}, volume={}, number={}, pages={1165-1172}, keywords={Sensors;Autonomous vehicles;Roads;Safety;Space vehicles;Time measurement;Security}, doi={10.1109/ITSC.2019.8917066} }

Ambrosin, Moreno and Yang, Lily L and Liu, Xiruo and Sastry, Manoj R and Alvarez, Ignacio J (2019). Design of a misbehavior detection system for objects based shared perception V2X applications. 2019 IEEE Intelligent Transportation Systems Conference (ITSC).

Human-system cooperation in automated driving

Published in ‘International Journal of Human Computer Interaction’, Elsevier 2019

Abstract

To draw a comprehensive and cohesive understanding of human–vehicle cooperation in automated driving, a review is made on key studies in human–robot interaction and human factors. Throughout this article, insight is provided into how human drivers and vehicle systems interplay and influence each other. The limitations of technology-centered taxonomies of automation are discussed and the benefits of accounting for human agents are examined. The contributions of machine learning to automated driving and how critical models in human-system cooperation can inform the design of a more symbiotic relationship between driver and vehicle are investigated. Challenges in the human element to enable the safe introduction of road automation are also discussed. Particularly, the unintended consequences of vehicle automation on driver’s workload, situation awareness and trust are examined, and the social interactions between driver, vehicle, and other road users are investigated. This review will help professionals shape future directions for safer and more efficient and effective human–vehicle cooperation.

Key Contributions

Critiques technology-centered automation taxonomies
Promotes human-centered automation design.
Analyzes automation’s impact on drivers
Reviews machine learning for cooperation
Examines social human-vehicle interactions

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Citation

@article{biondi2019human, title={Human--vehicle cooperation in automated driving: A multidisciplinary review and appraisal}, author={Biondi, Francesco and Alvarez, Ignacio and Jeong, Kyeong-Ah}, journal={International Journal of Human--Computer Interaction}, volume={35}, number={11}, pages={932--946}, year={2019}, publisher={Taylor \& Francis} }

Biondi, Francesco and Alvarez, Ignacio and Jeong, Kyeong-Ah (2019). Human-system cooperation in automated driving. International Journal of Human--Computer Interaction.

Human–vehicle cooperation in automated driving: A multidisciplinary review and appraisal

Published in ‘International Journal of Human Computer Interaction’, Elsevier 2019

Abstract

To draw a comprehensive and cohesive understanding of human–vehicle cooperation in automated driving, a review is made on key studies in human–robot interaction and human factors. Throughout this article, insight is provided into how human drivers and vehicle systems interplay and influence each other. The limitations of technology-centered taxonomies of automation are discussed and the benefits of accounting for human agents are examined. The contributions of machine learning to automated driving and how critical models in human-system cooperation can inform the design of a more symbiotic relationship between driver and vehicle are investigated. Challenges in the human element to enable the safe introduction of road automation are also discussed. Particularly, the unintended consequences of vehicle automation on driver’s workload, situation awareness and trust are examined, and the social interactions between driver, vehicle, and other road users are investigated. This review will help professionals shape future directions for safer and more efficient and effective human–vehicle cooperation.

Key Contributions

Critiques technology-centered automation taxonomies
Promotes human-centered automation design.
Analyzes automation’s impact on drivers
Reviews machine learning for cooperation
Examines social human-vehicle interactions

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Citation

@article{biondi2019human, title={Human--vehicle cooperation in automated driving: A multidisciplinary review and appraisal}, author={Biondi, Francesco and Alvarez, Ignacio and Jeong, Kyeong-Ah}, journal={International Journal of Human--Computer Interaction}, volume={35}, number={11}, pages={932--946}, year={2019}, publisher={Taylor \& Francis} }

Biondi, Francesco and Alvarez, Ignacio and Jeong, Kyeong-Ah (2019). Human–vehicle cooperation in automated driving: A multidisciplinary review and appraisal. International Journal of Human--Computer Interaction.

Object-level perception sharing among connected vehicles

Published in Proceedings of 2019 IEEE Intelligent Transportation Systems Conference (ITSC), IEEE 2023

Abstract

Advances in vehicular communication technologies have made Connected Vehicles (CVs) a near-term reality. Compared to models where vehicles rely solely on their own perception to sense and make decision on the environment, CVs have the potential to allow information sharing among vehicles to improve sensing and decision making collectively. A first concrete step towards this goal is enabling vehicles to share perception-related information, to overcome limitations of their respective sensors (e.g., partial awareness due to occlusions). Sharing processed information vs raw sensor data has the advantage of reducing the amount of data to be transmitted, and the required computation burden at the receiver side.This paper proposes an approach to enable object-level sharing among vehicles. Following state-of-the-art object-level management techniques, we developed a two-layer architecture that handles object tracking and fusion from dynamic remote sources of information. We implemented our approach and showed that it can achieve realistic performance, and robustness both in terms of quality of information and computation.

Key Contributions

Proposes two-layer object fusion architecture
Defines connected vehicles as virtual sensors
Dynamically manages vehicles entering/leaving range
uses objects using distance-based clustering
Evaluates system accuracy and scalability

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@INPROCEEDINGS{8916837, author={Ambrosin, Moreno and Alvarez, Ignacio J and Buerkle, Cornelius and Yang, Lily L and Oboril, Fabian and Sastry, Manoj R and Sivanesan, Kathiravetpillai}, booktitle={2019 IEEE Intelligent Transportation Systems Conference (ITSC)}, title={Object-level Perception Sharing Among Connected Vehicles}, year={2019}, volume={}, number={}, pages={1566-1573}, keywords={Sensor fusion;Vehicle dynamics;Standards;Collaboration;Merging;Covariance matrices}, doi={10.1109/ITSC.2019.8916837} }

Alvarez, I. (2019). . 2019 IEEE Intelligent Transportation Systems Conference (ITSC).

The SKYNIVI experience: evoking startle and frustration in dyads and single drivers

Published in ‘Proceedings of the IEEE International Conference in Intelligent Transportations Systems 2019’, IEEE 2019

Abstract

To study naturalistic in-cabin emotion we developed SKYNIVI, a modified open source driving simulator, with scenarios designed to elicit startle and frustration. We target generating these emotions because we believe that by detecting these it will be possible for autonomous vehicles to learn to drive better. We show how to use SKYNIVI to develop datasets that capture naturalistic emotions in drivers and passengers for algorithmic development. We recruited 51 participants as dyads and single drivers to participate in two different scenarios. We show that we were able to evoke hundreds of instances of our target emotions in this cohort and present an analysis of factors we found to impact emotional expression including: scenario design , demographic factors, personality and baseline affect . We find that having a second person in the vehicle impacts observed expressions of emotion even when no difference in baseline affect is reported.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

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@INPROCEEDINGS{8813831, author={Alvarez, Ignacio and Healey, Jennifer and Lewis, Erica}, booktitle={2019 IEEE Intelligent Vehicles Symposium (IV)}, title={The SKYNIVI Experience: Evoking Startle and Frustration in Dyads and Single Drivers}, year={2019}, volume={}, number={}, pages={76-81}, keywords={Automobiles;Autonomous vehicles;Monitoring;Roads;Training}, doi={10.1109/IVS.2019.8813831} }

Alvarez, Ignacio and Healey, Jennifer and Lewis, Erica (2019). The SKYNIVI experience: evoking startle and frustration in dyads and single drivers. 2019 IEEE Intelligent Vehicles Symposium (IV).

Towards standardization of AV safety: C++ library for responsibility sensitive safety

Published in ‘Proceedings of the IEEE Intelligent Vehicle Symposium 2019’, IEEE 2019

Abstract

The need for safety in Automated Driving (AD) is becoming increasingly critical with the accelerating deployment of this technology. Beyond functional safety, industry must guarantee the operational safety of automated vehicles. Towards that end, Mobileye introduced the Responsibility Sensitive Safety (RSS), a model-based approach to Safety [1]. In this paper we expand upon this work introducing the C++ Library for Responsibility Sensitive Safety, an open source executable that implements a subset of RSS. We provide architectural details to integrate the C++ Library for Responsibility Sensitive Safety with AD Software pipelines as safety module overseeing decision making of driving policies. We illustrate this application with an example integration with the Baidu Apollo AD stack and simulator, [2] and [3], that provides safety validation of the planning module. Furthermore, we show how the C++ Library for Responsibility Sensitive Safety can be used to explore the usefulness of the RSS model through parameter exploration and analysis on minimum safe longitudinal distance, (dmin), considering different weather conditions. We also compare these results with half-of-speed rule followed in some parts of the world. We expect that the C++ Library for Responsibility Sensitive Safety becomes a critical component of future tools for formal verification, testing and validation of AD safety and that it helps bootstrap the AD research efforts towards standardization of safety.

Key Contributions

Safety analysis and validation
Standardized C++ library implementation
Modular software architecture design
Verification and validation methodology
Open-source safety model deployment

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Citation

@inproceedings{gassmann2019towards, title={Towards standardization of AV safety: C++ library for responsibility sensitive safety}, author={Gassmann, Bernd and Oboril, Fabian and Buerkle, Cornelius and Liu, Shuang and Yan, Shoumeng and Elli, Maria Soledad and Alvarez, Ignacio and Aerrabotu, Naveen and Jaber, Suhel and Van Beek, Peter and others}, booktitle={2019 IEEE Intelligent Vehicles Symposium (IV)}, pages={2265--2271}, year={2019}, organization={IEEE} }

Gassmann, Bernd and Oboril, Fabian and Buerkle, Cornelius and Liu, Shuang and Yan, Shoumeng and Elli, Maria Soledad and Alvarez, Ignacio and Aerrabotu, Naveen and Jaber, Suhel and Van Beek, Peter and others (2019). Towards standardization of AV safety: C++ library for responsibility sensitive safety. 2019 IEEE Intelligent Vehicles Symposium (IV).

Agents, environments, scenarios: A framework for examining models and simulations of human-vehicle interaction

Published in ‘Transportation research interdisciplinary perspectives’, 2020

Abstract

This paper provides a framework for examining human-vehicle interactions with respect to three dimensions that can involve models or simulations: the agents, the environments, and the scenarios. Agents are considered on a spectrum from human to artificial actors. Environments are considered on a spectrum from simulated to real. Scenarios are considered on a spectrum from constrained to unconstrained. It is argued that these three dimensions capture key differences in research approaches within the field of human-vehicle interaction, and that explicitly situating research and discussions within this framework will allow researchers to better compare and contrast research outcomes and contributions. The framework is used to locate different disciplines in the community with respect to one another, and to identify areas which are as-yet unexplored.

Key Contributions

Human-computer interaction design
Dimensions of simulation models
Automated Driving Development

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Citation

@article{janssen2020agents, title={Agents, environments, scenarios: A framework for examining models and simulations of human-vehicle interaction}, author={Janssen, Christian P and Boyle, Linda Ng and Ju, Wendy and Riener, Andreas and Alvarez, Ignacio}, journal={Transportation research interdisciplinary perspectives}, volume={8}, pages={100214}, year={2020}, publisher={Elsevier} }

Janssen, Christian P and Boyle, Linda Ng and Ju, Wendy and Riener, Andreas and Alvarez, Ignacio (2020). Agents, environments, scenarios: A framework for examining models and simulations of human-vehicle interaction. Transportation research interdisciplinary perspectives.

Auto-UI Global Perspectives

Published in ACM IX Interaction Journal, 2020

Abstract

ACM SIGCHI Auto-UI is a growing community, but one in which some continents were less involved than expected and hoped for. For the 2019 conference in Utrecht, the Netherlands, we made various targeted attempts to grow and diversify our international community, with support from the ACM SIGCHI Development Fund. Our efforts resulted in a growth in the number of Asian participants, which made up almost 20 percent of the attendees. In this blog, we briefly reflect on our initiatives and on a panel discussion focusing on research topics that matter more globally to the Auto-UI field.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

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Citation

@article{janssen2020auto, title={Auto-UI: global perspectives}, author={Janssen, Christian P and Schroeter, Ronald and Bidwell, Nicola J and Ji, Yong Gu and Alvarez, Ignacio and Bao, Shan and Jeon, Myounghoon and Boyle, Linda Ng and Donker, Stella F and Chuang, Lewis L and others}, journal={Interactions}, volume={27}, number={6}, pages={7--9}, year={2020}, publisher={ACM New York, NY, USA} }

Janssen, Christian P and Schroeter, Ronald and Bidwell, Nicola J and Ji, Yong Gu and Alvarez, Ignacio and Bao, Shan and Jeon, Myounghoon and Boyle, Linda Ng and Donker, Stella F and Chuang, Lewis L and others (2020). Auto-UI Global Perspectives. Interactions.

Emotion GaRage Vol. II A Workshop on Affective In-Vehicle Display Design

Published in ‘Proceedings of 12th International Conference in Automotive User Interfaces and Interactive Vehicular Applications’, 2020

Abstract

Driver performance and behavior can be partially predicated based on one’s emotional state. Through ascertaining the emotional state of passengers and employing various mitigation strategies, empathic cars can show potential in improving user experience and driving performance. Challenges remain in the implementation of such strategies, as individual differences play a large role in mediating the effect of affective intervention. Therefore, we propose a workshop that aims to bring together researchers and practitioners interested in affective interfaces and in-vehicle technologies as a forum for the development of targeted emotion intervention methods. During the workshop, we will focus on a common set of use cases and generate approaches that can suit different user groups. By the end of this short workshop, researchers will determine ideal intervention methods for prospective user groups. This will be achieved through the method of insight combination to generate and discuss ideas.

Key Contributions

Affective Computing in Automotive
Emotion-Aware HMI Design
Community Building

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Citation

@misc{nadri2020emotion, title={Emotion GaRage Vol. II}, author={Nadri, Chihab and Bosch, E and Oehl, M and Alvarez, I and Braun, M and Jeon, M}, year={2020}, publisher={II} }

Nadri, Chihab and Bosch, E and Oehl, M and Alvarez, I and Braun, M and Jeon, M (2020). Emotion GaRage Vol. II A Workshop on Affective In-Vehicle Display Design. .

Evaluation of responsibility-sensitive safety (rss) model based on human-in-the-loop driving simulation

Published in In proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, IEEE 2020.

Abstract

Safety is an important challenge in the development of automated vehicles (AVs). To help with the challenge of achieving higher safety in the decision making of AVs, Intel and Mobileye have proposed a parameterized model named Responsibility-Sensitive Safety (RSS). Previous studies have demonstrated that RSS has the potential to improve the safety performance of automated vehicles. However, RSS could lead to a considerable car-following distance depending on the parameter values chosen for the model, which could reduce traffic efficiency. To improve the efficiency of RSS applied to Adaptive Cruise Control (ACC) systems, previous work proposed an efficiency-optimal (referred as “Efficiency-optimal RSS”) variation of the RSS model that involves different triggering conditions of a proper response. Therefore, in this paper a human-in-the-loop driving simulation experiment was conducted to evaluate the performance and acceptance of different safety methods. The RSS model and the efficiencyoptimal variant were embedded in an ACC system based on Model Predictive Control (MPC) algorithm. Two car-following scenarios with a sudden deceleration of lead vehicle at various time headways were simulated to evaluate the human perception and response of the different models. Results show that the original RSS model improves subjective safety judgment of human drivers. While the Efficiency-optimal RSS variant has a lower subjective safety score when compared to original RSS, it significantly reduces driver’s emergency braking reactions when compare to an ACC only system.

Key Contributions

Human-in-the-loop evaluation framework
Comparative performance analysis (RSS vs. Human)
Safety model parameter sensitivity analysis
Empirical validation of a formal safety model
Simulation-based model calibration methodology

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Citation

@inproceedings{chai2020evaluation, title={Evaluation of responsibility-sensitive safety (rss) model based on human-in-the-loop driving simulation}, author={Chai, Chen and Zeng, Xianming and Alvarez, Ignacio and Elli, Maria Soledad}, booktitle={2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)}, pages={1--7}, year={2020}, organization={IEEE} }

Alvarez, I. (2020). . 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC).

How safe is safe enough? Automatic safety constraints boundary estimation for decision-making in automated vehicles

Published in Proceedings of IEEE Intelligent Vehicle Symposium 2020, IEEE 2020.

Abstract

The determination of safety assurances for automated driving vehicles is one of the most critical challenges in the industry today. Several behavioral safety models for automated driving have been proposed recently and standards discussions are on the way. In this paper we present a method to automatically explore the performance of automated vehicle (AV) safety models utilizing robustness of Metric Temporal Logic (MTL) specifications as a continuous metric of safety. We present a case study of the Responsibility Sensitive Safety model (RSS), introducing a safety evaluation pipeline based on the CARLA driving simulator, RSS and a set of safety-critical driving scenarios. Our method automatically extracts safety relevant profiles for these scenarios providing practical parametric boundaries for implementation. Furthermore, we evaluate the trade-offs between safety and utility within the safe RSS parameter space through a proposed naturalistic benchmark challenge that we open-sourced. We analyze different RSS parameter configurations including assertive and more conservative settings, extracted by our specification-driven framework. Our results show that while maintaining the safety boundaries, the extracted RSS configuration for assertive driving behavior achieves the highest utility.

Key Contributions

Safety boundary estimation framework
Parametric and non-parametric estimation methods
Simulation-based data generation and validation
Real-world data application and analysis
Systematic safety parameter definition

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Citation

@inproceedings{rodionova2020safe, title={How safe is safe enough? Automatic safety constraints boundary estimation for decision-making in automated vehicles}, author={Rodionova, Al{"e}na and Alvarez, Ignacio and Elli, Maria Soledad and Oboril, Fabian and Quast, Johannes and Mangharam, Rahul}, booktitle={2020 IEEE Intelligent Vehicles Symposium (IV)}, pages={1457--1464}, year={2020}, organization={IEEE} }

Alvarez, I. (2020). . 2020 IEEE Intelligent Vehicles Symposium (IV).

PURSS: Towards Perceptual Uncertainty Aware Responsibility Sensitive Safety with ML.

Published in ‘Proceedings of 2020 AAAI Conference’, AAAI 2020

Abstract

Automated driving is an ML-intensive problem and its safety depends on the integrity of perception as well as planning and control. Responsibility Sensitive Safety (RSS) is a recent approach to promote safe planning and control that relies on perfect perception; however, perceptual uncertainty is always present, and this causes the possibility of misperceptions that can lead an autonomous vehicle to allow unsafe actions. In this position paper, we sketch a novel proposal for a formal model of perception coupled with RSS to help mitigate the impact of misperception by using information about perceptual uncertainty. The approach expresses uncertainty as imprecise perceptions that are consumed by RSS and cause it to limit actions to those that support safe behaviour given the perceptual uncertainty. We illustrate our approach using examples and discuss its implications and limitations.

Key Contributions

Safety analysis and validation

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Citation

@inproceedings{salay2020purss, title={PURSS: Towards Perceptual Uncertainty Aware Responsibility Sensitive Safety with ML.}, author={Salay, Rick and Czarnecki, Krzysztof and Elli, Maria Soledad and Alvarez, Ignacio J and Sedwards, Sean and Weast, Jack}, booktitle={SafeAI@ AAAI}, pages={91--95}, year={2020}, organization={New York} }

Salay, Rick and Czarnecki, Krzysztof and Elli, Maria Soledad and Alvarez, Ignacio J and Sedwards, Sean and Weast, Jack (2020). PURSS: Towards Perceptual Uncertainty Aware Responsibility Sensitive Safety with ML.. SafeAI@ AAAI.

MISO-V: Misbehavior detection for collective perception services in vehicular communications

Published in ‘Proceedings of IEEE Intelligent Transportation Systems Conference 2021’, IEEE 2021

Abstract

Recently, Collective Perception Messages (CPM) that carry additional information about the surrounding environment beyond Basic Safety Messages (BSM) or Cooperative Awareness Messages (CAM) have been proposed to increase the situational awareness for Connected and Automated Vehicles (CAV) in Intelligent Transportation Systems. However, blindly trusting perception information from neighbors that cannot be locally verified is dangerous given the safety impact that erroneous or malicious information might have. This paper addresses the data trust challenge of CPMs, proposing a misbehavior detection scheme called MISO- V (Multiple Independent Sources of Observations over V2X) that leverages the inherently overlapping nature of the perception observations from multiple vehicles to verify the semantic correctness of the V2X data and improve the data trust and robustness of V2X systems. CPM-enabled CAVs are implemented and MISO-V performance is evaluated in CARLA-based simulation tool, where falsified V2X packets presenting a ghost car are injected in a suburban T-junction scenario with other cars. The results show that MISO- V is very effective in detecting the ghost car attacks and removing the impact of such misbehavior from influencing the receiver and offers a conservative and sensible approach towards trustworthy Collective Perception Services for CAVs.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

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Citation

@INPROCEEDINGS{9575970, author={Liu, Xiruo and Yang, Lily and Alvarez, Ignacio and Sivanesan, Kathiravetpillai and Merwaday, Arvind and Oboril, Fabian and Buerkle, Cornelius and Sastry, Manoj and Baltar, Leonardo Gomes}, booktitle={2021 IEEE Intelligent Vehicles Symposium (IV)}, title={MISO- V: Misbehavior Detection for Collective Perception Services in Vehicular Communications}, year={2021}, volume={}, number={}, pages={369-376}, keywords={Simulation;Semantics;Redundancy;Receivers;Tools;Robustness;Safety}, doi={10.1109/IV48863.2021.9575970} }

Liu, Xiruo and Yang, Lily and Alvarez, Ignacio and Sivanesan, Kathiravetpillai and Merwaday, Arvind and Oboril, Fabian and Buerkle, Cornelius and Sastry, Manoj and Baltar, Leonardo Gomes (2021). MISO-V: Misbehavior detection for collective perception services in vehicular communications. 2021 IEEE Intelligent Vehicles Symposium (IV).

On responsibility sensitive safety in car-following situations-a parameter analysis on german highways

Published in Proceedings of the IEEE Intelligent Vehicles Symposium (IV), IEEE 2021

Abstract

The need for safety in automated driving is undisputed. Since automated vehicles are expected to reduce the number of fatalities in road traffic significantly, hundreds of millions of test kilometers would be required for statistical safety validation [1]. Physics-based safety verification approaches are promising in order to reduce this validation effort. Towards this goal, Mobileye introduced the concept of Responsibility-Sensitive Safety (RSS). In RSS, bounds for the reasonable worst-case behavior of traffic participants are assumed to be given, such as the reaction time or the maximum deceleration. These parameters have a crucial effect on the applicability of the approach: choosing conservative parameters likely hinders traffic flow, while the opposite could lead to collisions, as the assumptions are violated. Thus, in this work, we focus on finding reasonable parameters of RSS. Based on the physical limits, legal requirements and human driving behavior, we propose scopes and parameter sets that allow for a sound safety verification while not hindering traffic flow. Furthermore, we present an approach that explains seemingly frequent human drivers’ RSS violations on highways and may lead to a useful extension of RSS.

Key Contributions

Safety analysis and validation
Automated driving system development
Verification and validation methods
Parameter Analysis

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Citation

@INPROCEEDINGS{9575420, author={Naumann, Maximilian and Wirth, Florian and Oboril, Fabian and Scholl, Kay–Ulrich and Elli, Maria Soledad and Alvarez, Ignacio and Weast, Jack and Stiller, Christoph}, booktitle={2021 IEEE Intelligent Vehicles Symposium (IV)}, title={On Responsibility Sensitive Safety in Car-following Situations - A Parameter Analysis on German Highways}, year={2021}, volume={}, number={}, pages={83-90}, keywords={Law;Intelligent vehicles;Roads;Safety;Brakes;Responsibility-Sensitive Safety (RSS);Automated Driving (AD);Safety Verification;Parameter Analysis}, doi={10.1109/IV48863.2021.9575420}}

Alvarez, I. (2021). . 2021 IEEE Intelligent Vehicles Symposium (IV).

To err is human: The role of human derived safety metrics in an age of automated vehicles

Published in ‘SAE Technical Paper’, SAE 2021

Abstract

As industry races to complete technical development of automated driving systems (ADS), important questions are being raised about how to measure the safety of such systems and the overall safety of Automated Vehicles (AVs). Traffic safety engineers have for decades utilized metrics to assess the safety of human drivers and measurements such as Time To Collision (TTC) and Time Headway (THW) have proven to be a useful indicator of increased risk of an accident for human drivers. But what if we can do better with AVs? Are human driving derived risk metrics meaningful for a self-driving vehicle? Recently, the Institute for Automated Mobility (IAM) published a set of metrics defined specifically for self-driving vehicles that provide a thorough assessment of the safety of an AV. While humans must use estimation and cautious judgement to make decisions, AVs can use precise measurement techniques via sensors and correlate multiple sources of data in real time. Utilizing information such as the reaction time of the ADS, the braking capability of the AV and more, the IAM proposed metrics allow for the assessment of the safety of an AV to be accurately measured, not as a notion of approximated risk, but as a binary calculation of safety. In this paper we analyze, compare and contrast human driving, risk-oriented safety metrics with the more definitive metrics proposed for AVs. We answer important questions about the necessary evolution of human derived metrics to ensure they are meaningful in the assessment of the safety of an AV, as well as whether novel metrics proposed for AVs can be used to better understand and assess the safety performance of AVs when compared to historical safety measures. Our research proves that AV-based assessment metrics can provide better insight into the safety of both AVs and human drivers.

Key Contributions

Safety analysis and validation
Automated driving system development

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Citation

@techreport{weast2021err, title={To err is human: The role of human derived safety metrics in an age of automated vehicles}, author={Weast, Jack and Elli, Maria and Alvarez, Ignacio}, year={2021}, institution={SAE Technical Paper} }

Weast, Jack and Elli, Maria and Alvarez, Ignacio (2021). To err is human: The role of human derived safety metrics in an age of automated vehicles. .

Driver emotions in automated vehicles

Abstract

There is little doubt that driving generates emotional responses, whether that’s the joy of freedom, the boredom of stop-and-go traffic or anger towards unsafe maneuvers. In this chapter we provide an overview of emotion research applied to the automotive context and highlight the impact of emotional states in varying levels of driving automation. We review the most critical research findings on the impact of emotional states in driving performance including reaction time and take-over readiness. We also discuss the application of emotion regulation strategies related to the driving task. Finally, we analyze the research challenges still present for robust emotional classification and personalization in their application to in-vehicle interactions. This technology offers great potential for the development of emotionally-aware in-cabin driver assistants which will play a critical role in the future of automated driving user experience development.

Key Contributions

Foundational theory compilation
UX design methods and frameworks
Interface application and case studies
Academia-industry knowledge transfer
Holistic user-centered design advocacy

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Citation

@incollection{fakhrhosseini2022driver, title={Driver emotions in automated vehicles}, author={FakhrHosseini, Shabnam and Ko, Sangjin and Alvarez, Ignacio and Jeon, Myounghoon}, booktitle={User Experience Design in the Era of Automated Driving}, pages={85--97}, year={2022}, publisher={Springer} }

Alvarez, I. (2022). . User Experience Design in the Era of Automated Driving.

Emotion GaRage Vol. III: A Workshop on Affective In-Vehicle Display Applications

Published in ‘Adjunct Proceedings of the 14th International Conference on Automotive User Interfaces and Interactive Vehicular Applications’, ACM 2022

Abstract

Empathic in-vehicle interfaces can address driver affect and mitigate decreases in driving performance and behavior that are associated with emotional states. Empathic vehicles can detect and employ a variety of intervention modalities to change user affect and improve user experience. Challenges remain in the implementation of such strategies, as a broader established view of practical intervention modalities and strategies is still absent. Therefore, we propose a workshop that aims to bring together researchers and practitioners interested in affective interfaces and in-vehicle technologies as a forum for the development of displays and alternatives suitable to various use case situations in current and future vehicle states. During the workshop, we will focus on a common set of use cases and generate approaches that can suit different user groups. By the end of this workshop, researchers will create a design flowchart for in-vehicle affective display designers when creating displays for an empathic vehicle.

Key Contributions

HCI Interaction Design
Emotional and affective computing
HCI Prototyping
Workshop organization and community building

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Citation

@inproceedings{10.1145/3544999.3550161, author = {Nadri, Chihab and Dong, Jiayuan and Li, Jingyi and Alvarez, Ignacio and Jeon, Myounghoon}, title = {Emotion GaRage Vol. III: A Workshop on Affective In-Vehicle Display Applications}, year = {2022}, isbn = {9781450394284}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3544999.3550161}, doi = {10.1145/3544999.3550161}, pages = {186–188}, numpages = {3}, keywords = {Empathic vehicles, affective computing, emotions, human-centered computing, interaction design, user experience}, location = {Seoul, Republic of Korea}, series = {AutomotiveUI '22} }

Nadri, Chihab and Dong, Jiayuan and Li, Jingyi and Alvarez, Ignacio and Jeon, Myounghoon (2022). Emotion GaRage Vol. III: A Workshop on Affective In-Vehicle Display Applications. .

Empathic vehicle design: Use cases and design directions from two workshops

Published in ‘Proceedings of ACM’s Computer Human Interaction Conference (CHI)’, ACM 2022

Abstract

Empathic vehicles are expected to improve user experience in automated vehicles and to help increase user acceptance of technology. However, little is known about potential real-world implementations and designs using empathic interfaces in vehicles with higher levels of automation. Given advances in affect detection and emotion mitigation, we conducted two workshops (N1 =24, N2 = 22, Ntotal = 46) on the design of empathic vehicles and their potential utility in a variety of applications. This paper recapitulates key opportunities in the design and application of empathetic interfaces in automated vehicles which emerged from the two workshops hosted at the ACM AutoUI conferences.

Key Contributions

Emphathetic HMI design in Automotive
Generative AI prototyping
Workshop organization and community building

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Citation

@inproceedings{nadri2022empathic, title={Empathic vehicle design: Use cases and design directions from two workshops}, author={Nadri, Chihab and Alvarez, Ignacio and Bosch, Esther and Oehl, Michael and Braun, Michael and Healey, Jennifer and Jallais, Christophe and Ju, Wendy and Li, Jingyi and Jeon, Myounghoon}, booktitle={CHI Conference on Human Factors in Computing Systems Extended Abstracts}, pages={1--7}, year={2022} }

Nadri, Chihab and Alvarez, Ignacio and Bosch, Esther and Oehl, Michael and Braun, Michael and Healey, Jennifer and Jallais, Christophe and Ju, Wendy and Li, Jingyi and Jeon, Myounghoon (2022). Empathic vehicle design: Use cases and design directions from two workshops. CHI Conference on Human Factors in Computing Systems Extended Abstracts.

IEEE Standard for Assumptions in Safety-Related Models for Automated Driving Systems

Abstract

This standard applies to road vehicles. It defines a minimum set of reasonable assumptions and foreseeable scenarios that shall be considered in the development of safety related models that are part of an automated driving system (ADS). Scope: This standard applies to road vehicles. For a set of scenarios, a minimum set of assumptions regarding reasonably foreseeable behaviors of other road users are defined that shall be considered in the development of safety-related models for automated driving systems (ADS). This standard further defines a list of attributes common to contributed safety-related models and methods to help verify whether a safety-related model takes the minimum set of assumptions into consideration. An informative annex instantiates several examples of how the proposed minimum set of assumptions could be employed in ADS development. Sources of uncertainty, such as prediction or perception errors, are out of scope to this standard. This standard does not guarantee the safety of the overall system in all scenarios. Purpose: Government and Industry alike need an open, transparent, and technology-neutral standard that provides guidance useful for evaluating the performance of an ADS. This guidance consists of a minimum set of assumptions with bounds on reasonably foreseeable behaviors of other road users used in the development of safety-related models.

Key Contributions

Minimum assumption definition for ADAS
Reasonable Foreseeable scenario specification
Technology-neutral evaluation guidance
Model verification methodology

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BibTeX

@ARTICLE{9761121, author={}, journal={IEEE Std 2846-2022}, title={IEEE Standard for Assumptions in Safety-Related Models for Automated Driving Systems}, year={2022}, volume={}, number={}, pages={1-59}, keywords={IEEE Standards;Autonomous vehicles;Vehicle safety;Autonomous driving;Vehicle driving;Data privacy;Decision making;ADS;ADS-operated vehicle;automated driving system;assumption;automated vehicle;autonomous vehicles;AV;decision-making;IEEE 2846}, doi={10.1109/IEEESTD.2022.9761121}}

Quantifying Error Propagation in Multi-Stage Perception System of Autonomous Vehicles via Physics-Based Simulation

Published in Proceedings of the IEEE 2022 Winter Simulation Conference, IEEE 2023

Abstract

Ensuring the safety of autonomous vehicle (AV) relies on accurate prediction of error occurrences in its perception system. Due to the inter-stage functional dependence, the error occurred at a certain stage may be propagated to the following stage and generate extra errors. To quantify the error propagation, this paper adopts the physics-based simulation, which enables fault injection at different stages of an AV perception system to generate error event data for error propagation modeling. A multi -stage Hawkes process (MSHP) is proposed to predict the error occurrences in each stage, with error propagation represented as a latent triggering mechanism. With explicitly considering the error propagation mechanism, the proposed outperforms benchmark methods in predicting error occurrence in a physics-based simulation of a multistage AV perception system. The proposed two-step likelihood-based algorithm accurately estimates the model coefficients in a numerical simulation case study.

Key Contributions

Integrated simulation and modeling framework
Fault-injection simulation for data generation
Statistical error propagation modeling
Model parameter estimation algorithm
Model validation and performance benchmarking

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Citation

@inproceedings{pan2022quantifying, title={Quantifying error propagation in multi-stage perception system of autonomous vehicles via physics-based simulation}, author={Pan, Fenglian and Zhang, Yinwei and Head, Larry and Liu, Jian and Elli, Maria and Alvarez, Ignacio}, booktitle={2022 Winter Simulation Conference (WSC)}, pages={2511--2522}, year={2022}, organization={IEEE} }

Alvarez, I. (2022). . 2022 Winter Simulation Conference (WSC).

Special Issue

Published in ‘MDPI Sensors’, 2022

Abstract

Connected and automated driving technologies have the potential to revolutionize transportation by facilitating mobility services to a wider population, improving safety and traffic efficiency. Automated driving technology is expected to reduce the number of accidents caused by human error and avert deadly crashes, ensure mobility for all, including old and impaired individuals, allow the human driver to perform alternative (secondary) tasks, increase traffic flow efficiency, reduce fuel consumption, and lower emissions.

Driven by these goals, humankind is experiencing an exponential growth in vehicle automation taking over the monitoring of surroundings and vehicle control tasks from human drivers in a quest towards full autonomy. Connected and automated vehicles are equipped with multimodal sensors that allow continuous perception and monitoring of driving tasks to assist drivers in lower levels of SAE automation or to fully take control of driving tasks under full SAE automation. Numerous sensors, both inside and outside vehicles, allow the detection and identification of oncoming obstacles, the determination of their velocity, and the prediction of future behaviours to avoid potential collisions. Each sensor has its own strengths and weaknesses in terms of range, accuracy, energy consumption. and sensitivity towards external conditions such as weather and light. Automated vehicles usually rely on a mix of signals to improve operational reliability and robustness under the dynamic external conditions of real-world deployments. Generally, we can divide external AV sensors into two major groups: active and passive. Active sensors generate an active signal (electromagnetic or light) transmitted to the external environment to analyse its reflection (e.g., radar, lidar), whereas passive sensors just record the information from the environment (e.g., camera). Additionally, there have been advances in intelligent transportation infrastructure to monitor road users, perform predictive analytics, and facilitate collaborative perception services and remote vehicle control.

The increasing commercial availability of conditional automation (SAE level 3) and the incoming Robotaxi services (SAE Level 4) have also resulted in an increase in in-cabin monitoring sensors dedicated to monitoring driver and passenger behaviours. Multimodal in-cabin monitoring systems are crucial enablers for successfully managing automated vehicle operations. These systems enable the detection of the driver/passenger’s physiological state and activity to assess their readiness to take over control of the vehicle if required as well as to monitor their safety. Driving monitoring solutions provide information on occupants’ fatigue, distraction, discomfort, and stress. Furthermore, they can help to verify that automation is used properly by evaluating engagement in the driving monitoring task or the inherent risk of the non-driving tasks.

This Special issue aims to collect original theoretical or empirical articles on different sensing technologies, solutions, and applications for automated vehicles.

Key Contributions

Research methodology development
Experimental design and analysis
Technical implementation and validation

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Citation

@article{SpecialIssue, author = {Ignacio Alvarez, Jaka Sodnik and Nikolas Thomopoulos}, title = {Robust Multimodal Sensing for Automated Driving Systems}, journal = {Sensors}, year = {2022}, volume = {22}, issue = {13}, pages = {}, doi = {ISSN 1424-8220} }

Ignacio Alvarez, Jaka Sodnik and Nikolas Thomopoulos (2022). Special Issue. Sensors.

User experience design in the era of automated driving

Published in Journal Name, 2023

Abstract

This book is dedicated to user experience design for automated driving to address humane aspects of automated driving, e.g., workload, safety, trust, ethics, and acceptance. Automated driving has experienced a major development boost in recent years. However, most of the research and implementation has been technology-driven, rather than human-centered. The levels of automated driving have been poorly defined and inconsistently used. A variety of application scenarios and restrictions has been ambiguous. Also, it deals with human factors, design practices and methods, as well as applications, such as multimodal infotainment, virtual reality, augmented reality, and interactions in and outside users. This book aims at 1) providing engineers, designers, and practitioners with a broad overview of the state-of-the-art user experience research in automated driving to speed-up the implementation of automated vehicles and 2) helping researchers and students benefit from various perspectives and approaches to generate new research ideas and conduct more integrated research.

Key Contributions

State-of-the-art UX Design in Automated Driving
Foundational theory compilation
UX design methods and frameworks
Interface application and case studies

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Citation

@book{riener2022user, title={User experience design in the era of automated driving}, author={Riener, Andreas and Jeon, Myounghoon and Alvarez, Ignacio}, volume={980}, year={2022}, publisher={Springer} }

Alvarez, I. (2022). . .

White Paper-Literature Review on Kinematic Properties of Road Users for Use on Safety-Related Models for Automated Driving Systems

Published in IEEE Standards, 2022

Abstract

This document presents a review of relevant literature (e.g., standards, regulations, and scientific publications) that investigated kinematic behavior of road users. This review is intended to serve as a key contribution to the Automated Driving Systems (ADS) research and industry communities, as well as to current standardization efforts, such as IEEE Std 2846, IEEE Standard for Assumptions in Safety-Related Models for Automated Driving Systems.

Key Contributions

Literature review road actors kinematic behavior
Systematic data organization of kinematic parameter values
Kinematic data contextualization
Research gap analysis

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Citation

@ARTICLE{9763462, author={IEEE VT, ITS}, journal={Literature Review on Kinematic Properties of Road Users for Use on Safety-Related Models for Automated Driving Systems}, title={Literature Review on Kinematic Properties of Road Users for Use on Safety-Related Models for Automated Driving Systems}, year={2022}, volume={}, number={}, pages={1-35}, keywords={ADS;automated driving systems;behaviors;IEEE 2846;kinematic;literature review;road users;standards;white paper}, doi={} }

Alvarez, I. (2022). . Literature Review on Kinematic Properties of Road Users for Use on Safety-Related Models for Automated Driving Systems.

Application of Responsibility-Sensitive Safety in Areas with Limited Visibility: Occlusions in RSS

Published in ‘2023 IEEE 26th International Conference on Intelligent Transportation Systems’, IEEE 2023

Abstract

Areas of limited visibility are common in day-to-day traffic: be it static buildings, parked vehicles, traffic participants blocking the vehicle’s line of sight, harsh weather conditions or just narrow curves that impede the automated driving sensor suite to inspect the road ahead. Autonomous vehicles have to be able to safely cope with this kind of constraints. The Responsibility-Sensitive Safety model (RSS) demands vehicles to exercise caution with respect to occlusions and to consider also occluded road agents. This paper provides a concrete implementation of how occlusions in RSS can be addressed and investigates the balance between safety and usefulness of the model when a reasonably foreseeable behavior of occluded road agents is assumed. We perform occlusion experiments in urban as well as on highway scenarios with the driving simulation platform CARLA applying different parameterization of the agents kinematic properties and the safety model parameters to analyse and judge the consequences with respect to safe driving and overcautious driving behaviors.

Key Contributions

Safety analysis and validation
Implements occlusion handling with artificial vehicles
Evaluates approach in urban/highway scenarios
Shows aggressive parameters can reduce usability
Analyzes real-world data for assumptions
Demonstrates collision mitigation via simulation

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Citation

@INPROCEEDINGS{10421912, author={Gassmann, Bernd and Dey, Shreya and Alvarez, Ignacio and Oboril, Fabian and Scholl, Kay-Ulrich}, booktitle={2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)}, title={Application of Responsibility-Sensitive Safety in Areas with Limited Visibility: Occlusions in RSS}, year={2023}, volume={}, number={}, pages={5976-5981}, keywords={Roads;Kinematics;Safety;Behavioral sciences;Usability;Intelligent transportation systems;Meteorology}, doi={10.1109/ITSC57777.2023.10421912} }

Gassmann, Bernd and Dey, Shreya and Alvarez, Ignacio and Oboril, Fabian and Scholl, Kay-Ulrich (2023). Application of Responsibility-Sensitive Safety in Areas with Limited Visibility: Occlusions in RSS. 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC).

Emotion GaRage Vol. IV: Creating empathic in-vehicle interfaces with generative AIs for automated vehicle contexts

Published in ‘Proceedings of ACM’s Automotive UI 2023’, ACM 2023

Abstract

This workshop aims to design advanced empathic user interfaces for in-vehicle displays, particularly for high-level automated vehicles (SAE level 3 or higher). Incorporating model-based approaches for understanding human emotion regulation, it seeks to enhance the user-vehicle interaction. A unique aspect of this workshop is the integration of generative artificial intelligence (AI) tools in the design process. The workshop will explore generative AI’s potential in crafting contextual responses and its impact on user experience and interface design. The agenda includes brainstorming on various driving scenarios, developing emotion-oriented intervention methods, and rapid prototyping with AI tools. The anticipated outcome includes practical prototypes of affective user interfaces and insights on the role of AI in designing human-machine interactions. Through this workshop, we hope to contribute to making automated driving more accessible and enjoyable.

Key Contributions

Automated driving system development
Emotional and affective computing
Designs advanced empathic user interfaces
Integrates generative AI in design process
Applies model-based emotion regulation methods

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Citation

@inproceedings{10.1145/3581961.3609828, author = {Choe, Mungyeong and Bosch, Esther and Dong, Jiayuan and Alvarez, Ignacio and Oehl, Michael and Jallais, Christophe and Alsaid, Areen and Nadri, Chihab and Jeon, Myounghoon}, title = {Emotion GaRage Vol. IV: Creating Empathic In-Vehicle Interfaces with Generative AIs for Automated Vehicle Contexts}, year = {2023}, isbn = {9798400701122}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3581961.3609828}, doi = {10.1145/3581961.3609828}, booktitle = {Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages = {234–236}, numpages = {3}, keywords = {ChatGPT, affective computing, emotions, empathic vehicles, interaction design}, location = {Ingolstadt, Germany}, series = {AutomotiveUI '23 Adjunct} }

Choe, Mungyeong and Bosch, Esther and Dong, Jiayuan and Alvarez, Ignacio and Oehl, Michael and Jallais, Christophe and Alsaid, Areen and Nadri, Chihab and Jeon, Myounghoon (2023). Emotion GaRage Vol. IV: Creating empathic in-vehicle interfaces with generative AIs for automated vehicle contexts. Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

“Play Your Anger”: A report on the empathic in-vehicle interface workshop

Published in ‘Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications’, 2023

Abstract

Empathic in-vehicle interfaces are critical in improving user safety and experiences. There has been much research on how to estimate drivers’ affective states, whereas little research has investigated intervention methods that mitigate potential impacts from the driver’s affective states on their driving performance and user experiences. To enhance the development of in-vehicle interfaces considering emotional aspects, we have organized a workshop series to gather automotive user interface experts to discuss this topic at the International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI). The present paper focuses particularly on the intervention methods created by the experts and proposes design recommendations for future empathic in-vehicle interfaces. We hope this work can spark lively discussions on the importance of drivers’ affective states in their user experience of automated vehicles and pose the right direction.

Key Contributions

Presents novel empathic interface prototypes.
Offers expert-driven design recommendations.
Addresses anger, frustration, and fear.
Details a three-phase design process.
Focuses on specific intervention methods.

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Citation

@inproceedings{10.1145/3581961.3609865, author = {Dong, Jiayuan and Nadri, Chihab and Alvarez, Ignacio and Diels, Cyriel and Lee, Myeongkyu and Li, Jingyi and Liao, Pei Hsuan and Manger, Carina and Sadeghian, Shadan and Schu\ss{}, Martina and Walker, Bruce N. and Walker, Francesco and Wang, Yiyuan and Jeon, Myounghoon}, title = {“Play Your Anger”: A Report on the Empathic In-vehicle Interface Workshop}, year = {2023}, isbn = {9798400701122}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3581961.3609865}, doi = {10.1145/3581961.3609865}, booktitle = {Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, pages = {168–173}, numpages = {6}, location = {Ingolstadt, Germany}, series = {AutomotiveUI '23 Adjunct} }

Dong, Jiayuan and Nadri, Chihab and Alvarez, Ignacio and Diels, Cyriel and Lee, Myeongkyu and Li, Jingyi and Liao, Pei Hsuan and Manger, Carina and Sadeghian, Shadan and Schu\ss{ (2023). “Play Your Anger”: A report on the empathic in-vehicle interface workshop. Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.

RSS Demonstrator: a Tool for User Experience Interactions with Automated Driving Safety Models

Published in ‘Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications’, 2023

Abstract

Safety Assurance remains a challenge for the large-scale deployment of Automated Driving Systems (ADS). Safety models monitor the performance of the ADS. Most safety models are validated both in simulation and during on-road tests. However, first-hand experiences and analysis of ADS safety models are not easily accessible to the general research community. This paper introduces the RSS driving demonstrator an open-source simulation tool that enables first-hand experience of the Responsibility Sensitive Safety (RSS) safety model proposed by Intel and Mobileye and adopted by several Automotive Industry standards and regulatory frameworks. The RSS demonstrator enables first-hand interactions and experience of ADS safety model restrictions in both automated and manual driving conditions. As a User Experience (UX) tool, it provides quantitative safety metrics and flexible user interaction features. The results indicate it served to both evangelize the RSS ADS safety model with laymen population and is a versatile tool for Automotive UX development.

Key Contributions

Safety analysis and validation
User experience design and evaluation
Automated driving system development
Tool development and demonstration
Human-computer interaction design

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Citation

@inproceedings{10.1145/3581961.3609894, author = {Alvarez, Ignacio and Gassmann, Bernd and Pasch, Frederik and Oboril, Fabian}, title = {RSS Demonstrator: a Tool for User Experience Interactions with Automated Driving Safety Models}, year = {2023}, isbn = {9798400701122}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3581961.3609894}, doi = {10.1145/3581961.3609894}, pages = {180–185}, numpages = {6}, keywords = {Automated Driving Safety Model, Automated Driving System, Driving Simulation, RSS, Safety, User Experience}, location = {Ingolstadt, Germany}, series = {AutomotiveUI '23 Adjunct} }

Alvarez, Ignacio and Gassmann, Bernd and Pasch, Frederik and Oboril, Fabian (2023). RSS Demonstrator: a Tool for User Experience Interactions with Automated Driving Safety Models. .

Inside Out: Emotion GaRage Vol. V

Published in ‘Adjunct Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications’, 2024

Abstract

The rapid advancement of automated vehicles has aroused the curiosity of researchers in the automotive field. Understanding the emotional aspects of this technology is critical to improving human-vehicle interactions. The topics of the proposed workshop will be expanded from internal to external empathetic interface designs of automated vehicles. The workshop will gather researchers and practitioners to brainstorm and design affective internal and external interfaces for automated vehicles, targeting specific use cases within the social context. During the workshop, participants will use an affective design tool and generative AI to prototype affective interface designs in automated vehicles. With this creative approach, we aim to expand the knowledge of affective eHMIs in addition to in-vehicle designs and understand social factors that contribute to the user perceptions of automated vehicles.

Key Contributions

Emotional and affective computing
Expands focus to external vehicle interfaces
Aims to create design guidelines
Addresses social context and ethical issues
Prototype affective interface designs

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Citation

@inproceedings{10.1145/3641308.3677403, author = {Dong, Jiayuan and Gowda, Nikhil and Wang, Yiyuan and Choe, Mungyeong and Alsaid, Areen and Alvarez, Ignacio and Krome, Sven and Jeon, Myounghoon}, title = {Inside Out: Emotion GaRage Vol. V}, year = {2024}, isbn = {9798400705205}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3641308.3677403}, doi = {10.1145/3641308.3677403}, pages = {260–263}, numpages = {4}, keywords = {affective external human-machine interaction designs, emotions, empathic in-vehicle interfaces, generative artificial intelligence}, location = {Stanford, CA, USA}, series = {AutomotiveUI '24 Adjunct} }

Dong, Jiayuan and Gowda, Nikhil and Wang, Yiyuan and Choe, Mungyeong and Alsaid, Areen and Alvarez, Ignacio and Krome, Sven and Jeon, Myounghoon (2024). Inside Out: Emotion GaRage Vol. V. .

Reliability modeling for perception systems in autonomous vehicles: A recursive event-triggering point process approach

Published in ‘Transportation Research Part C: Emerging Technologies’, 2024

Abstract

Ensuring the reliability of sensor-fusion-based perception systems is crucial for the safe deployment of autonomous vehicles. Such systems function through a sequence of interconnected stages, where errors in upstream stages may propagate to downstream stages and trigger additional errors. The cross-stage error propagation conceptually exists and makes errors in different stages, not independent, posing model challenges, estimation challenges, and data challenges for reliability modeling. The existing methods cannot be applied to address all these challenges. Thus, this paper presents a recursive event-triggering point process to explicitly consider the error propagation based on the simulated data. The data are simulated from a proposed error injection framework, which can generate various errors from a sequence of interconnected stages in a perception system. The latent and probabilistic error propagation information is incorporated into a modified expectation–maximization (EM) algorithm for parameter estimation. The numerical and physics-based simulation case studies demonstrate the prediction accuracy and interpretability of the proposed modeling methodology.

Key Contributions

Automated driving system development
Proposes recursive event-triggering point process
Develops modified EM estimation algorithm
Presents simulation error injection framework
Differentiates primary and propagated errors
Models redundant multi-module perception systems

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Citation

@article{pan2024reliability, title={Reliability modeling for perception systems in autonomous vehicles: A recursive event-triggering point process approach}, author={Pan, Fenglian and Zhang, Yinwei and Liu, Jian and Head, Larry and Elli, Maria and Alvarez, Ignacio}, journal={Transportation Research Part C: Emerging Technologies}, volume={169}, pages={104868}, year={2024}, publisher={Elsevier} }

Pan, Fenglian and Zhang, Yinwei and Liu, Jian and Head, Larry and Elli, Maria and Alvarez, Ignacio (2024). Reliability modeling for perception systems in autonomous vehicles: A recursive event-triggering point process approach. Transportation Research Part C: Emerging Technologies.

Challenges and Opportunities in Automated Driving in the Era of Agentic AI

I recently had the pleasure of speaking to the students of the Máster de Movilidad Cooperativa Conectada y Autónoma at Universidad Carlos III de Madrid, sharing how Generative AI is reshaping every corner of automated‑driving R&D. My session, “Challenges and Opportunities in Automated Driving in the Era of Agentic AI,” argued that these new AI models are far more than hype, they mark a genuine shift in the way we design, test, and ultimately will trust autonomous vehicles. Here are a few highlights from the talk content.

Generative AI Landscape

I started by defining what Generative AI means to me. I find that these terms are often overused, and end up meaning different things to different people. In my mind, Generative AI is a subset of Deep Learning, a group of algorithms that create new data rather than merely classify existing inputs. Whether through GANs, VAEs, diffusion pipelines, Transformers, or state‑space networks, these algorithms learn rich distributions of data representations from enormous data corpora and when trained, they become foundation models such as GPT‑n. Unlike hand‑coded software, they’re trained statistical models that approximate facets of human cognition. Based on the type of data they were trained we will have multiple “flavors” of foundation models with different capabilities.

So LLMs are not computer programs, they are “models”. Here is an analogy I like: if a computer program is like a recipe with precise steps, a model, is like having a chef who has learned to cook by tasting countless dishes and understanding flavor combinations. You can ask this “chef” to create new dishes based on this data-driven (learned) understanding, even if they haven’t seen that exact recipe before.

But LLMs are made of code. The “program” in this case is the architecture of the neural network and the learning algorithm, while the “model” is the resulting set of learned weights and biases within that network.

Generative AI cook

After getting our basis, I turned to the autonomous‑vehicle (AV) pipeline itself, where applications today cover the complete AV development stack. From simulation for realistic synthetic world generation as well as verification and validation, to perception problems, creating maps, predicting road user behaviors and new conversational AI assistants for passengers.

On simulation, I touched on solutions like Scenario Diffusion from Zoox that conjure complex, multi‑agent traffic scenes conditioned on HD maps and textual prompts, or UniSim by Waabi, which transforms mundane driving logs into editable, photorealistic worlds. These synthetic environments slash the expense and risk of road testing while letting engineers probe rare edge cases at scale.

On perception, I showcased DatasetGAN, an NVIDIA technique that simplifies manual labeling tasks and wiht a handful of labeled images spanws vast troves of perfectly segmented data.

One of the highlights on planning applicaitons is BehaviorGPT that impressed everyone with its ability to predict myriad plausible trajectories for every road user, capturing the uncertainty of traffic.

And as we get closer to the frontier of end‑to‑end decision making. DriveGPT4 shows how fused video perception with language‑model reasoning, can output low‑level control commands and natural‑language justifications for human consumption.

GenAI Pipeline

Of course, no talk is complete without a reality check. I emphasized the twin troubles of scarce, high‑quality multimodal data and the scary computational appetite of today’s large models. Achieving sub‑100‑millisecond latency for LLMs on in‑car hardware is no trivial feat. Security and safety are very real threats (think prompt‑injection attacks, IP leakage, nondeterministic outputs, and opaque reasoning) all of these challenge regulators and engineers alike.

Yet the opportunities are equally compelling. Model‑compression tricks like quantization, pruning, and distillation can turn bloated networks into nimble binaries, while neuromorphic chips such as Intel’s Loihi handle spike‑based inference with astonishing energy efficiency. Safety, too, can be engineered: binding language‑model suggestions inside a rigorously proven shell like Mobileye’s RSS keeps behaviors within an acceptable envelope, and tools like LVLM Interpret begin to open the model’s “black box,” offering visibility into why a recommendation was made.

Generative AI Landscape

I closed by reminding people that the march towards full autonomy is a marathon, but Generative AI has armed us with fresh answers to old questions. If we address data, performance, security, and safety head‑on, we can unlock a future of safer, more efficient, and more intelligent mobility. The technology is here to stay; our task now is to wield it ethically, safely, and wisely.

Thanks for reading! Contact me if you think the full content of this talk would be of interest to you or and audience of your choice.

—Prof. Ignacio Alvarez

Future of AI Transport

Study design and implementation of UX tests: AV harrassment

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Description

This course is designed to prepare students for their final thesis. It provides fundamental knowledge to develop a study design based on a problem definition, conduct the study, evaluate it, and interpret the results. Thematic clusters in the area of human-computer interaction (e.g., productivity, automated driving, sports, and digitalization) are offered, from which student groups can choose and propose a specific topic. This topic will be developed iteratively in close professional coordination with the respective supervising lecturers. • Basics of user studies (possible applications, definition of research hypothesis) • Study design (dependent/independent variables, laboratory vs. field studies, within-groups/between-groups design) • Planning of experiments (different methods, “fidelity” of an experiment, software/hardware prototypes, Wizard of Oz studies, qualitative surveys/quantitative measurements, ethical aspects, role of an institutional review board (IRB)) • Study implementation (preparation, briefing/debriefing, finding and inviting test subjects, determining group size, learning effects, measuring variables/data collection) • Qualitative data analysis (content analysis, evaluation with MAXQDA/NVIVO, preparation and presentation of results, revision/fine-tuning in Illustrator) • Quantitative data analysis (reflection on the research hypothesis, use of SPSS for statistical evaluations, parametric/nonparametric statistics, correct choice of method, presentation and interpretation of results) • Written/oral presentation of the results (preparation of results, peer review process, final conference-style presentation)

Exploring and Addressing Aggression, Vandalism, Harassment and Abuse of Autonomous Robots

Recent developments highlight the growing role of automated systems in urban mobility. For example, autonomous delivery robots (ADRs), which facilitate contactless delivery, were increasingly used to support lastmile logistics during the COVID-19 pandemic. Additionally, Waymo’s robotaxis have recently completed 150,000 commercial rides and cover approximately 1.6 million kilometers per week. While these developments offer promising mobility solutions, ensuring broad public acceptance is crucial. One of the challenges currently faced is the risk of vandalism, harassment, and aggression against these systems, which can significantly impact safety, usability, and public trust. Addressing these concerns requires a proactive design approach that mitigates risks and enhances security. Within this seminar, students analyzed aspects of aggression, vandalism, harassment, and abuse directed at autonomous robots (e.g. delivery service robots, automated vehicles) with the goal to develop interfaces and technological solutions that mitigate these risks while ensuring the vehicles remain safe and functional for all users. Based on an individual literature review (related work), students (in groups of two or three) identified key challenges and explored innovative concepts. be developed together in a dedicated brainstorming session and refined/substantiated by the individual teams (with feedback from the lecturer).

Student Project Details

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Interested?

Contact me if you’d like me to teach this course to you or your audience.

Study design and implementation of UX tests: Emphatic Car

Course Icon

Description

This course is designed to prepare students for their final thesis. It provides fundamental knowledge to develop a study design based on a problem definition, conduct the study, evaluate it, and interpret the results. Thematic clusters in the area of human-computer interaction (e.g., productivity, automated driving, sports, and digitalization) are offered, from which student groups can choose and propose a specific topic. This topic will be developed iteratively in close professional coordination with the respective supervising lecturers. • Basics of user studies (possible applications, definition of research hypothesis) • Study design (dependent/independent variables, laboratory vs. field studies, within-groups/between-groups design) • Planning of experiments (different methods, “fidelity” of an experiment, software/hardware prototypes, Wizard of Oz studies, qualitative surveys/quantitative measurements, ethical aspects, role of an institutional review board (IRB)) • Study implementation (preparation, briefing/debriefing, finding and inviting test subjects, determining group size, learning effects, measuring variables/data collection) • Qualitative data analysis (content analysis, evaluation with MAXQDA/NVIVO, preparation and presentation of results, revision/fine-tuning in Illustrator) • Quantitative data analysis (reflection on the research hypothesis, use of SPSS for statistical evaluations, parametric/nonparametric statistics, correct choice of method, presentation and interpretation of results) • Written/oral presentation of the results (preparation of results, peer review process, final conference-style presentation)

This course provides a practical introduction to the integration of generative AI and “vibe coding” into UX work-flows. Students will: • Explore foundational concepts of Generative AI, focusing on LLMs. • Learn techniques of prompt engineering to effectively direct AI tools. • Apply AI-driven methods to enhance UX research processes, from data collection to synthesis and prototyping. • Utilize AI-assisted development platforms, such as ChatGPT, Replit or Cursor, to prototype interactive user interfaces, tools and applications. • Investigate the emerging paradigm of Agentic AI and its potential to autonomously resolve user experience challenges. • Undertake a comprehensive final project, culminating in designing, building, and deploying a specialized AI agent addressing a defined UX issue.

Student Project Details

Coming soon

Interested?

Contact me if you’d like me to teach this course to you or your audience.