Further research, testing, and safety features should be developed to help drivers manage the transition from automated to manual control of a vehicle.
A detailed analysis of California Department of Motor Vehicle crash records examines the most frequent types of automated vehicle crashes.
Made Public Date
03/25/2020

14

San Francisco
California
United States
TwitterLinkedInFacebook
Identifier
2020-00941

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

Background

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.

METHODS

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.

Lessons Learned

  • Further research, testing, and safety features should be developed to assist drivers as they transition from automated to manual control of a vehicle. Analysis of automated vehicle crashes in California shows that 30 percent of crashes occur while transitioning from automated to manual control or vice versa. Of the crashes associated with transitioning, the majority occurred when the driver initiated a transition and not when the vehicle initiated the transition. These findings may partially validate concerns over the extensive workload placed on drivers in automated mode who are tasked with monitoring both challenging roadway conditions and the automated systems.

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

Crash Themes in Automated Vehicles: A Topic Modeling Analysis of the California Department of Motor Vehicles Automated Vehicle Crash Database
Publication Sort Date
01/01/2020
Author
Alambeigi, Hananeh; Anthony McDonald; and Srinivas Tankasala
Publisher
Texas A&M University

(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.)

Goal Areas

Focus Areas Taxonomy: