  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.
