Naturalistic Driving Data Baseline for Automated Driving System-Equipped Commercial Motor Vehicles

Automated driving system (ADS) equipped commercial motor vehicles (CMVs) have the potential to reduce crashes by eliminating driver inattention and negative behavior errors. Assessing the effectiveness of ADS-equipped CMVs requires an understanding of baseline safety performance measures for non-equipped CMVs. This study analyzed naturalistic event field data including over 3.44 billion miles of Class 8 CMV operations to produce a lagging performance baseline of crashes and near-crashes. This baseline represents rates produced over long periods of time across large numbers of CMVs. The event data for the lagging baseline was map-matched to 10 specific highway operational design domains (ODDs), including all U.S. highways that fit the study criteria and appeared in the data, and the nine specific highways where many of the data were collected. This study also analyzed naturalistic continuous field data including 3.2 million miles of Class 8 CMV operations to produce a leading performance baseline of typical CMV maneuvers. This baseline represents rates of vehicle maneuvers—that is, how frequently a given maneuver occurs per vehicle mile traveled within specific domains. The leading baseline was developed from six types of maneuvers including speed behavior, longitudinal deceleration, following distance, lateral acceleration, lane deviation, and lane stability. Maneuvers were matched across 10 U.S. highway ODDs, 7 speed limit categories, and 7 number-of-lane categories. A reference set of leading baseline performance was also developed for a collection of Canada highway data. These data may be valuable for creating baselines applicable to northern U.S. transit corridors or to traffic between the United States and Canada. This study also resulted in a public-use data tool for querying event rates based on a range of selectable parameters.

• Record URL:
  https://rosap.ntl.bts.gov/view/dot/57506
• Corporate Authors:

  Virginia Tech Transportation Institute

  Blacksburg, Virginia  United States 

  Federal Motor Carrier Safety Administration

  Office of Analysis, Research, and Technology
  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590
• Authors:
  • Krum, Andrew
  • Miller, Andrew
  • Sarkar, Abhijit
  • Engstrom, Johan
  • Soccolich, Susan
  • Grove, Kevin
  • Hickman, Jeff
  • Hanowski, Richard
  • Ali, Gibran
• Publication Date: 2021-8

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Appendices; Figures; Maps; References; Tables;
• Pagination: 200p

Subject/Index Terms

• TRT Terms: Automatic data collection systems; Automation; Commercial vehicle operations; Crash rates; Driver performance; Traffic platooning
• Subject Areas: Data and Information Technology; Motor Carriers; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01782725
• Record Type: Publication
• Report/Paper Numbers: FMCSA-RRT-19-017
• Contract Numbers: DTMC7514D00011L; DTMC7517F00058
• Files: NTL, TRIS, ATRI, USDOT
• Created Date: Sep 24 2021 10:19AM