United States Department of Transportation
i
Automatic Detection and Localization of Roadwork
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2023-07-31
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[PDF-1.95 MB]
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Edition:Final Report
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Abstract: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).
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