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Automated vehicle technology promises to reduce the societal impact oftraffic crashes. Early investigations of this technology suggest thatsignificant safety issues remain during control transfers between theautomation and human drivers and automation interactions with thetransportation system. In order to address these issues, it is critical tounderstand both the behavior of human drivers during these events and theenvironments where they occur. This article analyzes automated vehicle crashnarratives from the California Department of Motor Vehicles automated vehiclecrash database to identify safety concerns and gaps between crash types andcurrent areas of focus in the current research. The database was analyzed usingprobabilistic topic modeling of open-ended crash narratives. Topic modelinganalysis identified five themes in the database: driver-initiated transitioncrashes, sideswipe crashes during left-side overtakes, and rear-end collisionswhile the vehicle was stopped at an intersection, in a turn lane, and when thecrash involved oncoming traffic. Many crashes represented by thedriver-initiated transitions topic were also associated with the side-swipecollisions. A substantial portion of the side-swipe collisions also involvedmotorcycles. These findings highlight previously raised safety concerns withtransitions of control and interactions between vehicles in automated mode andthe transportation social network. In response to these findings, futureempirical work should focus on driver-initiated transitions, overtakes, silentfailures, complex traffic situations, and adverse driving environments. Beyondthis future work, the topic modeling analysis method may be used as a tool tomonitor emergent safety issues.

Crash Themes in Automated Vehicles: A Topic Modeling Analysis of the California Department of Motor Vehicles Automated Vehicle Crash Database