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.