American Journal of Student Research / 2026

A Review of AI Safety and Trustworthiness in Autonomous Vehicles

Elston Su

This narrative review examines how autonomous-vehicle safety depends on the combined roles of perception, robustness, explainability, and ethical alignment. It argues that reliable perception and robust model behavior form the technical foundation of safe decision-making, while explainability enables transparency, accountability, and system-level oversight. In addition, ethical considerations, including fairness, responsibility, and bias mitigation, are shown to be inseparable from safety in AI-driven driving systems. This article reviews recent research, comparing key technical challenges, robustness limitations, and interpretability requirements across autonomous driving architectures. The review identifies major gaps in existing work, including limited robustness under rare and unpredictable scenarios, insufficient dataset diversity, and the absence of unified safety and certification frameworks. It concludes that both industry and regulatory bodies must adopt stronger certification practices and responsible deployment strategies to ensure that autonomous-vehicle systems remain trustworthy, transparent, and safe.