An Artificial Intelligence (AI) Based Overheight Vehicle Warning System for Bridges

Details

• Creators:
  Agrawal, Anil ; Kumar, Deepak
• Corporate Creators:
  Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART) ; The City College of New York, CUNY
• Corporate Contributors:
  United States. Department of Transportation. Federal Highway Administration ; 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:
  • Artificial intelligence
  • Automatic vehicle detection and identification systems
  • Bridges
  • Cameras
  • Cost effectiveness
  • Height
  • Impact
  • Trucks
  • Virtual reality
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final report, 3/1/22-9/30/23
• Corporate Publisher:
  Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART)
• Abstract:
  Impact of overheight vehicles on bridges is a major problem that causes extensive damage and leads to frequent traffic and mobility issues. This research proposes conceptual development of a low-cost Artificial Intelligence- (AI)-based overheight vehicle warning system for bridges based on the use of cutting-edge camera technology and AI-based height detection. The proposed system consists of a long-range camera and an AI-based overheight detection module that can detect an overheight vehicle from safe stopping distance. Successful implementation of the proposed research will lead to the prevention of a significant number of impacts on bridges by overheight trucks, which will improve safety of bridges and traffic mobility, thereby saving millions of dollars in damage to bridges and preventing injuries and fatalities. However, further research is required to verify accuracy of detection and integration of hardware for implementation.
• Format:
  PDF
• Funding:
  69A3551747119
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/72429/dot_72429_DS1.pdf
• File Type:
  [PDF - 1.57 MB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:24f9564c9d63a15941acf9a52c3a29f33cd952df3bf1f6e3f306d14c15e4e04a18ab7ebf5c27c6bf0fdbd36603af4d7c99d16c85804885756e2332d954122d6f