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

Explainability is essential for public acceptance: The paper emphasizes that while autonomous vehicles can make safe decisions, their AI-guided decision-making processes are currently not understandable to humans, which "hinders this technology from being socially acceptable." This suggests that technical safety alone is insufficient—AVs must be able to explain their choices to gain public trust.

Regulatory compliance requires transparency: The paper identifies that explainability is necessary for AVs to be "regulatory compliant across many jurisdictions," indicating that governments are beginning to require that autonomous systems justify their decisions, not just produce safe outcomes.

XAI addresses a critical gap in current autonomous driving: The research highlights that despite significant progress in AI capabilities for sensing environments and making real-time decisions, there remains a fundamental deficiency: the intelligent decision-making in AVs is "not generally understandable by humans in the current state of the art." This gap between capability and explainability is a major barrier to deployment.

Explainability enhances multiple dimensions of trust: The paper proposes that XAI approaches can improve "transparency, trustworthiness, and societal acceptance of AVs," suggesting that explainability serves multiple purposes beyond just regulatory compliance—it builds confidence among users, regulators, and the public.

A comprehensive framework is needed: The authors developed a "conceptual framework that considers the essential elements for explainable end-to-end autonomous driving," implying that effective XAI for AVs requires systematic, integrated approaches rather than isolated technical fixes.