Explainable Artificial Intelligence for Trustworthy Decision-Making and Planning in Autonomous Driving

Abstract

Artificial Intelligence (AI) has become the core enabling technology for Autonomous Driving (AD) systems, significantly improving perception, planning and control performance. However, the extensive adoption of deep learning and End-to-End (E2E) architectures has also introduced severe black-box problems, resulting in limited transparency and weak interpretability in safety-critical driving scenarios. The pursuit of transparent and accountable decision-making in autonomous driving has positioned Explainable Artificial Intelligence (XAI) as a pivotal area of inquiry in recent years. This paper systematically reviews the major black-box challenges in AI-based autonomous driving, including perception-level representation ambiguity, multimodal fusion interpretability issues, decision-making and planning unexplainability, and E2E driving model opacity. Furthermore, representative XAI techniques are analyzed from both technical and functional perspectives. The limitations and future research directions of XAI for AD are discussed. Through a systematic examination of existing studies, this review aspires to lay a solid foundation for the advancement of safer and more dependable AD systems.