This survey reviews explainability methods for vision-based self-driving
systems trained with behavior cloning. 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, as well as the challenges that are specific to this
application. Second, methods providing explanations to a black-box self-driving
system in a post-hoc fashion are comprehensively organized and detailed. Third,
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.

这项调查综述了针对基于行为克隆训练的视觉自驾系统的可解释性方法。该综述从计算机视觉、深度学习、自动驾驶和可解释人工智能等多个研究领域收集贡献，并讨论了自驾车的解释性定义、动机、方法以及未来挑战。