Alambeigi, Hananeh; Anthony McDonald; and Srinivas Tankasala
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Partially automated vehicles (PAVs) are becoming increasingly common on US roadways. However, researchers and public safety officials still lack a clear understanding of how safe PAVs are, particularly as the technology rapidly evolves and a more thorough understanding of what types of crashes PAVs are involved in is crucial to advancing safety efforts and designing interventions to mitigate PAV crashes.


Researchers at Texas A&M University analyzed one of the most comprehensive crash databases in the nation, the California Department of Motor Vehicles Database (CDMV) to better understand what types of crashes PAVs are most commonly involved in. The CDMV crash database is particularly robust and contains extensive detail about the crashes recorded. The database contained records relating to 167 PAV crashes, recorded between October 2014 and June 2019. All these crashes occurred in or around the San Francisco Bay Area. Of these 167 crashes, the team used 114 crashes in their final analysis as they eliminated any crashes where the driver was operating the vehicle in manual mode.

After cleaning and preparing the data, the research team used a form of semantic analysis called probabilistic topic modeling to identify themes in each of the crashes and then classify these crashes based on the identified themes.
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Texas A&M University
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Crash Themes in Automated Vehicles: A Topic Modeling Analysis of the California Department of Motor Vehicles Automated Vehicle Crash Database
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