視覚ベース自動運転システムの説明可能性：レビューと課題
Explainability of deep vision-based autonomous driving systems: Review and challenges
行動クローニングで学習した視覚ベース自動運転システムの説明可能性手法を、事後説明と設計段階からの解釈性の両面から体系的にレビューし、課題と今後の研究方向を示したサーベイ論文。
著者: Éloi Zablocki, Hédi Ben-Younes, Patrick Pérez, Matthieu Cord
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
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.
