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.
(Our website has many links to other organizations. While we offer these electronic linkages for your convenience in accessing transportation-related information, please be aware that when you exit our website, the privacy and accessibility policies stated on our website may not be the same as that on other websites.)