Analysis of Contributing Factors in Crashes Involving Electric Vehicles and Vehicles with Automated Features

Details

• Creators:
  Harper, Corey ; Hendrickson, Chris ; Wang, Jiacheng
• Corporate Creators:
  Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC)
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  Autonomous Vehicles Crash Causes Crash Characteristics Crash Severity Electric Vehicles Regression Analysis
• Publication/ Report Number:
  489
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report July 1, 2024 – June 30, 2025
• Corporate Publisher:
  Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC)
• Abstract:
  Most light-duty vehicle (LDV) crashes occur due to human error. The National Highway Safety Administration (NHTSA) reports that eight percent of fatal crashes in 2018 were distraction-affected crashes, while close to ninety-four percent of all crashes occur in part due to human error. Crash avoidance features could reduce both the frequency and severity of light and heavy-duty vehicle crashes, primarily caused by distracted driving behaviors and/or human error by assisting in maintaining control or issuing alerts if a potentially dangerous situation is detected. As the automobile industry transitions to partial vehicle automation, newer crash avoidance technologies are beginning to appear more frequently in non-luxury vehicles such as the Honda Accord and Mazda CX-9. Additionally, the market penetration of electric vehicles (EVs) is increasing, in turn increasing the weight and size of vehicles on the road. However, the patterns and characteristics of crashes involving EVs or vehicles automated features have not been explored in much detail. This project develops a replicable, open, deployable model that can: 1) assess the distribution of crashes with automated features across factors such as weather conditions, vehicle speed, crash severity, pre-crash movement, facility type, and time of day, 2) estimate the relationship between contributing factors and the severity of crashes involving vehicles with automated features using regression analysis, and 3) assess the patterns and characteristics of crashes involving EVs.
• Format:
  PDF
• Funding:
  69A3552344811
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:94386846c17c37ec0a633e7079bb3d72742163f88c8851f1632687722e33c1ece456907638efbdcfe9ec832f9675b28d52a43ec184b976b14dcbf82e8aef6654
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/85713/dot_85713_DS1.pdf
• File Type:
  [PDF - 1.33 MB ]