Preventing Rear and Side Crashes of Heavy-Duty Tractor-Trailer Combinations with Smart Sensors and Vision Systems

Details

• Creators:
  Jahanbiglari, Ava ; Deng, Xiyu ; Nakahira, Yorie ; Tang, Pingbo
• 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 ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  Computer Vision Crash Avoidance Systems Crashes Heavy Duty Vehicles Intelligent Vehicles Near Crashes Rear End Crashes Sensors Side Crashes Tractor Trailer Combinations Traffic Safety

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• Publication/ Report Number:
  510
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report (July 1, 2024 – August 30, 2025)
• Corporate Publisher:
  Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC)
• Abstract:
  This project, conducted under the Safety21 University Transportation Center, advanced the safety of heavy- duty vehicles (HDVs) through physics-informed modeling, statistical analysis, sensor deployment, and socio- technical analysis. The research focused on two objectives: developing a Physics-Informed Reinforcement Learning (PIRL) framework for near-miss detection in tractor-trailers and designing customized safety sensor adoption strategies for small motor carriers. Methods included simulation of tractor-trailer dynamics to estimate safety probabilities in rear, side, and lane-keeping scenarios; probabilistic analysis of crash and inspection datasets to identify high-risk vehicle age–region groups; and survey-based studies of inspection practices modeled with an Inverse Contextual Bandit (ICB) framework. Industry interviews highlighted workforce shortages, data integration challenges, and trust in automation as barriers to adoption. Results showed PIRL outperforms standard Deep Q-Networks (DQN) in identifying near-miss risks, sensor adoption should be prioritized for specific high-risk fleets, and inspector decision-making adapts but remains inconsistent. Findings support tailored sensor programs, workforce training, and data interoperability standards to promote proactive, cost-effective HDV safety.
• Format:
  PDF
• Funding:
  69A3552344811, 69A3552348316
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:9024270ead8c3969b5619f00b97ce272d9e8a939547741b91bd8ec85deb6f99a0d3580352ef43b6f3968c80034f2cb54844a7e1e8279a7e762e3a2bfa825852e
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/86407/dot_86407_DS1.pdf
• File Type:
  [PDF - 2.43 MB ]