Divergent Effects of Factors on Crashes under Autonomous and Conventional Driving Modes Using A Hierarchical Bayesian Approach

Abstract: Influencing factors on crashes involved with autonomous vehicles (AVs) have been paid increasing attention. However, there is a lack of comparative analyses between influencing factors on crashes of AVs and human-driven vehicles. To fill this research gap, the study aims to explore the divergent effects of factors on crashes under autonomous and conventional driving modes. This study obtained 154 publicly available autonomous vehicle crash data (70 for the autonomous driving mode and 84 for the conventional driving mode), and 36 explanatory variables were extracted from three categories, including environment, roads, and vehicles. Then, a hierarchical Bayesian approach was applied to analyze the impacting factors on crash type and severity under both driving modes. The results showed that some factors affected both driving modes, but their degrees were different. For example, the presence of turning movement had a greater impact on the crash severity under the conventional driving mode, while the presence of turning movement led to a larger decrease in the likelihood of rear-end crashes under the autonomous driving mode. More influencing factors only had a significant impact on one of the driving modes. For example, in the autonomous driving mode, two sidewalks decreased the severity of crashes, and on-street parking was positively associated with rear-end crashes, but they were not significant in the conventional driving mode. This study could contribute to the understanding and development of autonomous driving systems and the better coordination between autonomous driving and conventional driving.