Analysis of Contributing Factors in Crashes Involving Electric Vehicles and Vehicles With Warning Systems and Level 1 Automated Features: A State Level Analysis [Final Report]

Details

• Creators:
  Harper, Corey ; Hendrickson, Chris ; Wang, Jiacheng
• Corporate Creators:
  Carnegie Mellon University
• Corporate Contributors:
  Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC) ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  • Heterogeneity
  • Autonomous vehicles
  • Crash causes
  • Crash characteristics
  • Crash severity
  • Electric vehicles
  • Regression analysis
  • Level 2 ADAS
  • Latent Class Analysis
  • Association Rule Mining
• Publication/ Report Number:
  69A3552344811 ; 69A3552348316
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC)
• Abstract:
  Automated driving features could improve road safety by reducing the number of crashes caused by human error. However, as the market penetration of Level 2 advanced driver assistance systems (ADAS) increases, so does the number of crashes involving these technologies. This study proposes a data-driven framework combining latent class analysis, association rule mining, and logistic regression to analyze 617 Level 2 ADAS crashes reported to the National Highway Traffic Safety Administration between 2019 and 2026. Two latent classes are identified, which are lower-speed local road crashes and high-speed highway crashes. Association rule mining and logistic regression are conducted within each latent class, revealing substantial heterogeneity in injury-related crash patterns. In lower-speed local road crashes, injury occurrences are mainly associated with frontal impacts, intersection-related crashes, and moderate-speed configurations. In high-speed highway crashes, injury occurrences are mainly associated with high-speed configurations, fixed objects, light-to-medium duty vehicles, frontal impacts, and morning or late-night conditions. Practical applications include placing greater emphasis in scenario-based testing for combinations involving moderate and high pre-crash speeds, frontal contact, fixed-object crashes, light-to-medium duty vehicle crash partners, and late-night conditions. These findings may help manufacturers refine speed-aware system-use guidance, driver warnings, and operational design constraints for Level 2 ADAS-equipped vehicles. Overall, this study demonstrates the value of class-specific analysis for uncovering heterogeneous injury-associated patterns in Level 2 ADAS crashes.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/93525/dot_93525_DS1.pdf
• File Type:
  [PDF - 861.62 KB]
• Collection(s):
  Autonomous Vehicles ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:0600ec850e810c7f45b145138920b5427c91cafc957a10d63b926f0cff2fbbb76384666c44098ac499c5c0413396f2e51bf9ac7761e3c41f1a6e34634e0ff788