An AI-based Oversize Vehicle Warning System in Smart Work Zone

Lane closures, when required during road repair and maintenance, can cause traffic congestion in adjacent open lanes. It is problematic when oversized vehicles are present, as they can create safety risks for workers and other drivers in work zones. The existing technologies in this regard are customized only for overheight vehicle detection and ignore the horizontal span of the vehicles. Therefore, those solutions cannot be extended directly to address the problem at hand. Additionally, the existing methods rely on expensive sensors such as LiDars and radars for automated vehicle detection. Exorbitant costs restrict the large-scale use of those devices. As a more economical solution, this study will leverage inexpensive Ref Green Blue-Depth (RGB-D) sensors for accurate learning-based vehicle size estimation. To address this issue, this project aims to develop an intelligent early warning system that uses low-cost 3D sensing cameras and artificial intelligence (AI)-based detection algorithms. The system will estimate the size of approaching vehicles and issue a real-time warning to any vehicle that is too large for the open lanes. This will help prevent potential accidents and encourage these vehicles to take alternate routes or slow down to ensure everyone's safety.

Language

• English

Project

• Status: Active
• Funding: $248,332.90
• Contract Numbers:

  69A3552348307

• 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:

  Mid-America Transportation Center

  University of Nebraska-Lincoln
  2200 Vine Street, PO Box 830851
  Lincoln, NE  United States  68583-0851
• Project Managers:

  Stearns, Amy

• Performing Organizations:

  Missouri University of Science & Technology, Rolla

  Department of Engineering
  202 University Center
  Rolla, MO    65409
• Principal Investigators:

  Chen, Genda

• Start Date: 20240601
• Expected Completion Date: 20260630
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Lane closure; Machine learning; Real time control; Vehicle size; Warning systems; Work zone safety; Work zone traffic control
• Subject Areas: Construction; Data and Information Technology; Highways; Operations and Traffic Management; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01971871
• Record Type: Research project
• Source Agency: Mid-America Transportation Center
• Contract Numbers: 69A3552348307
• Files: UTC, RIP
• Created Date: Nov 20 2025 4:26PM