Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions

Autonomous driving has achieved significant milestones in research and development over the last decade. There is increasing interest in the field as the deployment of self-operating vehicles promises safer and more ecologically friendly transportation systems. With the rise of computationally powerful artificial intelligence (AI) techniques, autonomous vehicles can sense their environment with high precision, make safe real-time decisions, and operate reliably without human intervention. However, intelligent decision-making in autonomous cars is not generally understandable by humans in the current state of the art, and such deficiency hinders this technology from being socially acceptable. Hence, aside from making safe real-time decisions, the AI systems of autonomous vehicles also need to explain how their decisions are constructed in order to be regulatory compliant across many jurisdictions. Our study sheds comprehensive light on the development of explainable artificial intelligence (XAI) approaches for autonomous vehicles. In particular, we make the following contributions. First, we provide a thorough overview of the state-of-the-art studies on XAI for autonomous driving. We then propose an XAI framework that considers the societal and legal requirements for the explainability of autonomous driving systems. Finally, as future research directions, we provide several XAI approaches that can improve operational safety and transparency to support public approval of autonomous driving technology by regulators and engaged stakeholders.