Using Naturalistic Driving Data to Assess Vehicle-to-Vehicle Crashes Involving Fleet Drivers

In-vehicle event recorders (IVERs) have become a widely accepted means of gathering crash data, both in research and real-world applications. In this study, the authors conducted the first-ever large-scale examination of naturalistic crash data. Other naturalistic studies have investigated only a small number of crashes or used near crashes as a proxy for real crashes. In contrast, this project examined hundreds of actual crashes from a naturalistic driving database. The data allowed for an examination of behaviors and potential contributing factors in the seconds leading up to the collision, and provided information not available in police reports. A coding scheme was developed specifically for this study, and video data were coded with the goal of identifying the factors that contributed to crashes—in particular the prevalence of potentially distracting driver behaviors and drowsiness. The study addressed the following research questions: (1) What were the roadway and environmental conditions at the time of the crash? (2) What were the critical events and potential contributing factors leading up to the crash and did these differ by crash type? (3) What driver behaviors were present in the vehicle prior to the crash and did these differ by crash type? (4) How did driver response times and eyes-off-road time differ relative to certain driver behaviors and crash types? (5) Could drowsy driving be detected using this type of crash data?

• Record URL:
  https://aaafoundation.org/wp-content/uploads/2017/12/VehicletoVehicleCrashesInvolvingFleetDriversReport.pdf
• Summary URL:
  https://aaafoundation.org/using-naturalistic-driving-data-assess-vehicle-vehicle-crashes-involving-fleet-drivers/
• Supplemental Notes:
  • ©2015, AAA Foundation for Traffic Safety
• Corporate Authors:

  AAA Foundation for Traffic Safety

  607 14th Street, NW, Suite 201
  Washington, DC    20005
• Authors:
  • Carney, Cher
  • 0000-0001-6741-4433
  • McGehee, Dan
  • Harland, Karisa
  • Weiss, Madonna
  • Raby, Mireille
• Publication Date: 2015-6

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 20p

Subject/Index Terms

• TRT Terms: Automatic data collection systems; Behavior; Crash causes; Crash characteristics; Crash data; Crashes; Distraction; Drivers; Drowsiness; Vehicle fleets; Video
• Subject Areas: Highways; Safety and Human Factors; I83: Accidents and the Human Factor;

Filing Info

• Accession Number: 01569006
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 30 2015 9:30AM