Explainability of vision-based autonomous driving systems: Review and challenges

• URL: http://arxiv.org/abs/2101.05307v1
• Date: Wed, 13 Jan 2021 19:09:38 GMT
• Title: Explainability of vision-based autonomous driving systems: Review and challenges
• Authors: \'Eloi Zablocki, H\'edi Ben-Younes, Patrick P\'erez, Matthieu Cord
• Abstract summary: The need for explainability is strong in driving, a safety-critical application. This survey gathers contributions from several research fields, namely computer vision, deep learning, autonomous driving, explainable AI (X-AI)
• Score: 33.720369945541805
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This survey reviews explainability methods for vision-based self-driving systems. The concept of explainability has several facets and the need for explainability is strong in driving, a safety-critical application. Gathering contributions from several research fields, namely computer vision, deep learning, autonomous driving, explainable AI (X-AI), this survey tackles several points. First, it discusses definitions, context, and motivation for gaining more interpretability and explainability from self-driving systems. Second, major recent state-of-the-art approaches to develop self-driving systems are quickly presented. Third, methods providing explanations to a black-box self-driving system in a post-hoc fashion are comprehensively organized and detailed. Fourth, approaches from the literature that aim at building more interpretable self-driving systems by design are presented and discussed in detail. Finally, remaining open-challenges and potential future research directions are identified and examined.

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