How similar or different are automated vehicle and human-driven vehicle crash patterns? Findings from crash sequence analysis

Abstract

Given the current state of vehicle automation, understanding the similarities and differences between Automated Vehicle (AV) and Human-driven Vehicle (HDV) crashes is crucial to identifying specific challenges and areas for improvement of AV technology. The challenges of directly comparing AV and HDV crashes include differing traffic environments, crash reporting discrepancies, and underreporting of HDV crashes. To address these challenges 555 AV crashes and 39,270 HDV crashes are used to perform a Crash Sequence Analysis. Results show that while AV and HDV crashes can be classified into similar groups based on vehicle movements, the types of crashes can differ significantly. While intersections pose greater challenges for AVs compared to HDVs, the severity of AV crashes is lower, which might be attributed to the reduced number of crashes involving AVs and vulnerable road users. Considering similar crash contexts, AV and HDV crashes can differ significantly, especially in scenarios involving pedestrians, left and right turns, the stopping movement of AVs, and red light violations. Furthermore, the analysis shows three groups with contexts unique to the AV crashes (rear end crashes following a lane change or stopped vehicles, and side swipe crashes on narrow streets), which can indicate both technological challenges and the differences in crash exposure caused by the manufacturers’ training environment. The above emphasizes the importance of addressing expectancy violations likely to emerge in a mixed fleet environment of AVs and HDVs, particularly accounting for geographical and cultural specificities. These findings provide key insights for shaping future AV development, guiding public sector decisions, and building public trust in automation.