  Automated vehicle technology promises to reduce the societal impact of
traffic crashes. Early investigations of this technology suggest that
significant safety issues remain during control transfers between the
automation and human drivers and automation interactions with the
transportation system. In order to address these issues, it is critical to
understand both the behavior of human drivers during these events and the
environments where they occur. This article analyzes automated vehicle crash
narratives from the California Department of Motor Vehicles automated vehicle
crash database to identify safety concerns and gaps between crash types and
current areas of focus in the current research. The database was analyzed using
probabilistic topic modeling of open-ended crash narratives. Topic modeling
analysis identified five themes in the database: driver-initiated transition
crashes, sideswipe crashes during left-side overtakes, and rear-end collisions
while the vehicle was stopped at an intersection, in a turn lane, and when the
crash involved oncoming traffic. Many crashes represented by the
driver-initiated transitions topic were also associated with the side-swipe
collisions. A substantial portion of the side-swipe collisions also involved
motorcycles. These findings highlight previously raised safety concerns with
transitions of control and interactions between vehicles in automated mode and
the transportation social network. In response to these findings, future
empirical work should focus on driver-initiated transitions, overtakes, silent
failures, complex traffic situations, and adverse driving environments. Beyond
this future work, the topic modeling analysis method may be used as a tool to
monitor emergent safety issues.
