Aug 2026· International Conference on Automation, Control and Robotics Engineering· pp. 155-160· 0 citations· 29 references
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.
This survey delivers a comprehensive and critical synthesis of the emerging role of GenAI across the autonomous driving stack, delving into the frontier applications of GenAI in image, LiDAR, trajectory, occupancy, and video generation, as well as LLM-guided reasoning and decision-making.
Yu-Ping Wang, Shuo Xing, C. Cui et al.· ACM Computing Surveys· 60 citations· ⚡2
While intelligent driving systems have achieved notable advances in perception, prediction, and motion planning, robust high-level decision-making remains a persistent challenge in long-tail scenarios that demand semantic scene understanding, contextual reasoning, and experience-based judgment. Rulebased policies tend...
Qiu-Man Xu, Shuai Zhao· 2026 3rd International Confe...· 0 citations
This 2026 survey synthesises 39 primary sources selected from an initial corpus of 274 candidate records screened over 2020-2026, organises the field around a five-role pipeline taxonomy (perception, prediction, planning, control, and human-machine interaction), and compares six representative driving MLLMs on accuracy...
A. O. Ogar, Joshua Abah, Ali Muhammad et al.· Journal of Science Research...· 0 citations
A deep neural network is developed to interpret complex traffic scenes and propose driving behavior, while an optimization-based supervision layer validates this proposal and enforces explicit drivability and safety constraints.
Jean-Pierre Busch, Guido Linden, Jan Bergmann et al.· 0 citations
A human-centred explainability framework is proposed that repositions AV explanation as a cognitive human-machine interaction problem, leveraging multimodal large language models as the generative engine for contextualised natural language explanations responsive to user expertise and situational urgency.
A. Rakotonirainy, A. Y. Zadeh, Zishuo Zhu et al.· AHFE International· 0 citations
The Proxy-to-Decision Transfer (PDT) Framework is proposed, an analysis framework that evaluates when learned future information supports a reliable driving-performance improvement claim, and identifies where decision transfer fails or remains indeterminate across proxy, subset, aggregate, and selection evidence.
Yi-Kai Wu· 0 citations
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