Autonomous Vehicle Safety: A Comprehensive Analysis of Crash Injury Determinants

Traffic-related fatalities are a significant global challenge that is primarily attributed to human error. Autonomous vehicles (AVs) hold the promise of mitigating such fatalities by removing the potential for human error and thereby enhancing road safety; however, they too are involved in crashes that result in injuries. Prior research, limited by the scope of the dataset and depth of analysis, has not fully explored these factors, resulting in a lingering lack of clarity about the cause of the crashes. This study analyzed 358 AV crash records from California’s Department of Motor Vehicles’ database for the period spanning from 2014 to July 2023. A bagging classifier model was utilized to predict the likelihood of injuries occurring in AV crashes, and various features of the vehicles were evaluated to determine their impact on crash outcomes. The findings revealed vehicle damage as the key predictor of injury risk, with a nonlinear damage-injury relationship. Characteristics specific to manufacturers were also found to play an important role, demonstrating their differences in technology and safety features. The study underscored the influence of vehicle and collision types on injury occurrences, particularly highlighting the vulnerability of two-wheeler drivers and the prevalence of sideswipe and rear-end collisions. Additionally, it revealed pre-collision movement patterns, especially those involving stationary vehicles, as crucial risk factors. The impact of different road types, like streets and avenues, on crash severity was also noted, further enriching the analysis. The findings offer valuable implications for AV manufacturers, policymakers, and urban planners in enhancing vehicle safety features.