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

The proposed project aims to prevent fatal rear and side crashes related to heavy-duty tractor-trailer combinations. Specifically, the research team proposes to develop and test smart trailer sensors/vision systems that infer "dynamic safety zones” and use lighting signals (or other communication modes) to alarm following and overtaking vehicles, pedestrians, or other non-occupant situations. The proposed trailer sensors/vision systems automatically analyze videos, vehicle size, and loading and brake data to infer collision risks between tractor-trailer combinations and approaching vehicles and people. From 2019 to 2021, fatal rear crashes with large trucks with trailers, where passenger vehicles travel under the rear of the truck, increased from 16.8% to 18.0%. In 2021, other vehicles in the large truck lane (26.5%) and others encroaching into the large truck lane (36.0%) were the two critical pre-crash events that caused such crashes. Drivers usually underestimate the required distance when the safe distance suddenly increases because of the large weights and sizes of the vehicles, unexpected pavement conditions, and terrains that require extra separations between vehicles. Inter-vehicle dynamic safety zones change and differ by situations and changes over time, so manually estimating the safe following and overtaking distances could be unreliable. Sometimes, illusions, slipperiness caused by weather, and poor lighting conditions can bias human estimates and make the reaction too late to stop. The recent integration of computer vision and motion sensors has shown the potential to improve passenger vehicles. However, heavy-duty vehicles, especially trailers, need special consideration of vehicle size, motion planning, road conditions, and occlusions to ensure a reliable assessment of side and rear collision risks in different positions of the tractors and trailers. The proposed project will integrate the expertise of the project team and two industry partners in developing and testing an intelligent tractor-trailer sensor and vision system and provide benchmark datasets. In construction and airport safety, the project team has integrated computer vision, robotic motion simulation, and spatiotemporal analyses to implement dynamic safety zone estimation solutions for aircraft and construction equipment. The project team has also developed the technique to find safe actions when there is uncertainty in the dynamic system models or environments. The proposed project will adapt these intelligent dynamic safety zone estimation solutions to implement the proposed smart sensors and vision systems on tractor-trailer combinations. An industry collaborator, Clarience Technologies, will work with the project team to use their tractor and trailer fleet to collect video, vehicle, and telematics data to support the development and testing of the proposed smart safety system. Clarience will also leverage its automotive and vehicular engineering background to support the 4D simulation and motion analysis of heavy-duty vehicles in given road and terrain conditions. Another industry partner, Safety Emissions Solutions, has collaborated with the team in integrating inspection reports, crash, and telematic data into ‘vehicle deterioration models’ that predict the crash risks of heavy-duty vehicles. Integrating this expertise, software, data, and hardware from the researchers and industry will ensure the timely delivery of the proposed dynamic safety zone estimation solution and the benchmark data sets.

• Record URL:
  • https://ppms.cit.cmu.edu/projects/detail/510

Language

• English

Project

• Status: Active
• Funding: $210000
• Contract Numbers:

  69A3552344811

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Carnegie Mellon University

  Pittsburgh, PA  United States 

  Safety21 University Transportation Center

  Carnegie Mellon University
  Pittsburgh, PA  United States  15213
• Project Managers:

  Stearns, Amy

• Performing Organizations:

  Carnegie Mellon University

  Pittsburgh, PA  United States 

  Safety21 University Transportation Center

  Carnegie Mellon University
  Pittsburgh, PA  United States  15213
• Principal Investigators:

  Tang, Pingbo

• Start Date: 20240701
• Expected Completion Date: 20250630
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Computer vision; Crash avoidance systems; Heavy duty vehicles; Intelligent vehicles; Rear end crashes; Sensors; Side crashes; Tractor trailer combinations; Traffic safety
• Subject Areas: Data and Information Technology; Highways; Motor Carriers; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01933405
• Record Type: Research project
• Source Agency: Safety21 University Transportation Center
• Contract Numbers: 69A3552344811
• Files: UTC, RIP
• Created Date: Oct 13 2024 9:43AM