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