A Causation Analysis of Autonomous Vehicle Crashes

Recent studies affirm the potential of autonomous vehicles (AVs) in reducing traffic accidents and fatalities. This article presents an overview of on-road AV testing and analyzes crash data involving AVs. The study emphasizes fatalities and errors, comparing AVs to conventional human-driven vehicles or nonautonomous vehicles (non-AVs). Statistical analysis indicates that in most cases, human errors predominantly cause accidents, with responsibility falling on non-AV entities, such as bicyclists and motorcyclists, as well as adverse weather and lighting conditions. Notably, rear-end collisions are prevalent. AVs display superior sensing and processing capabilities, except in adverse weather and automation failure scenarios. Surprisingly, human drivers are accountable for most accidents, overshadowing the significance of AV error rates. Common collision causes between AVs and non-AVs encompass overspeeding, inadequate following distances, and decision-making deficiencies in non-AVs. The study shows the potential of AVs to enhance road safety while shedding light on areas requiring continued improvement.