MAPPING AI FAILURE MODES IN AUTONOMOUS VEHICLES: A STRUCTURED REVIEW OF CAUSES AND MITIGATION STRATEGIES
Akhil Dudhipala, Rahul Karne, Pavan Kumar Pativada · Indian Journal of Computer Science and Engineering · 2025
Autonomous vehicles (AVs) increasingly incorporate artificial intelligence (AI) to improve perception, decision-making, and control. Integrating AI within AVs will have benefits, including reduced road deaths, lower emissions, and increased mobility, but failures have led to severe, sometimes fatal, outcomes. This paper develops a typology of AI failure modes in AVs, categorizing root causes into five categories: (1) biases in training data, (2) faulty algorithms, (3) out-of-distribution scenarios, (4) perceptual faults, and (5) adversarial attacks. We discuss the safety implications of each failure mode using literature, real-world case studies, and other examples. We propose several recommended mitigation strategies, including improved data practices, stronger model design, better sensing, and adversarial robustness. We also suggest future research avenues and make suggestions for our colleagues who are researchers, engineers, and policymakers to enhance the reliability and trustworthiness of AI for autonomous driving.