CMU Roadwork Dataset [supporting dataset]

Abstract of the final report is stated below for reference: 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 authors seek 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/media/project_files/Final_Report_-_398.pdf
• 
• Summary URL:
  • https://ppms.cit.cmu.edu/projects/detail/398
• Dataset URL:
  • https://www.cs.cmu.edu/~ILIM/roadwork_dataset/
• Supplemental Notes:
  • The dataset supports report: Automatic Detection and Understanding of Roadworks, available at the URL above. This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  National University Transportation Center for Improving Mobility (Mobility21)

  Carnegie Mellon University
  Pittsburgh, PA  United States  15213

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Narasimhan, Srinivas
  • 0000-0003-0389-1921
  • Tamburo, Robert
  • 0000-0002-5636-9443
• Publication Date: 2023

Language

• English

Media Info

• Media Type: Dataset
• Dataset publisher:

  National University Transportation Center for Improving Mobility (Mobility21)

  Carnegie Mellon University
  Pittsburgh, PA  United States  15213

Subject/Index Terms

• TRT Terms: Data; Detection and identification systems; Image analysis; Work zones
• Subject Areas: Data and Information Technology; Highways;

Filing Info

• Accession Number: 01892555
• Record Type: Publication
• Contract Numbers: 69A3551747111
• Files: UTC, TRIS, ATRI, USDOT
• Created Date: Sep 11 2023 11:39AM