Automatic Detection and Understanding of Roadworks

Roadwork zones present a serious impediment to vehicular mobility. Whether new construction or maintenance is taking place, work in road environments cause lower vehicle speeds, congestion, increased risk of rear-end collisions, and more difficult maneuvering. Crowd-sourced navigation systems like Waze warn drivers of roadworks, but those data must be manually entered causing a distraction for the driver. Google maps now automatically shows roadworks, but those data are often slow to update and do not distinguish between active/inactive work zones or specify lane restrictions/changes. In the proposed work, the study team seeks to address these issues by developing computer vision and machine learning methods that will automatically identify and understand (e.g., lane closed and two lanes merge into one lane) road work zones. The calculated information can be shared with other drives and also enable dynamic route planning for navigation systems, driver assist systems, and self-driving cars for efficiently and safely maneuvering through or around road work zones. Moreover, a comprehensive view of road work activity in a region can be constructed from information shared by users. Such a view may prove to be a useful tool for optimizing traffic flow along detour routes (e.g., traffic lights stay green for longer to accommodate the additional volume).

• Record URL:
  https://ppms.cit.cmu.edu/projects/detail/398

Language

• English

Project

• Status: Completed
• Funding: $99945
• Contract Numbers:

  69A3551747111

• Sponsor Organizations:

  Carnegie Mellon University

  Mobility21 National USDOT UTC for Mobility of Goods and People
  Pittsburgh, PA  United States  15213

  Office of the Assistant Secretary for Research and Technology

  University Transportation Center Program
  ,    
• Managing Organizations:

  Carnegie Mellon University

  Mobility21 National USDOT UTC for Mobility of Goods and People
  Pittsburgh, PA  United States  15213
• Project Managers:

  Kline, Robin

• Performing Organizations:

  Carnegie Mellon University

  ,    
• Principal Investigators:

  Narasimhan, Srinivasa

• Start Date: 20221201
• Expected Completion Date: 20230630
• Actual Completion Date: 20230731
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Advanced driver information systems; Computer vision; Detection and identification systems; Machine learning; Work zones
• Subject Areas: Construction; Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01868145
• Record Type: Research project
• Source Agency: National University Transportation Center for Improving Mobility (Mobility21)
• Contract Numbers: 69A3551747111
• Files: UTC, RIP
• Created Date: Dec 21 2022 11:40AM