Ignacio Alvarez

Ignacio Alvarez

Professor and Principal Engineer in Intelligent Systems

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.