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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