United States Department of Transportation
i
Automated Safety Assessment of Rural Roadways Using Computer Vision
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2023-04-01
[PDF-4.14 MB]
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Edition:Final Report August 2022 to April 2023
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Abstract:Roadside elements play an important role in the number and severity of crashes. Rigid obstacles (trees, rocks, embankments, etc.), guardrails, clear zones, and side slopes are among the factors that might affect roadside safety. The Federal Highway Administration (FHWA) presented a rating system to help DOTs and transportation agencies make better decisions about improving road segments. However, the manual process of rating road segments is time consuming, inconsistent, and labor intensive. To this end, this project proposed an automated rating system based on images taken from Utah roadways. Utilizing machine-learning algorithms and Mandli images, the developed approach employs the FHWA rating system as the primary standard for assessing roadside safety. To provide more detailed information about safety conditions on the roadside, various computer vision algorithms have been developed to detect each roadside feature. The pre-trained models for available clear zone detection and side slope classification have also been established. A shape-file has been generated by assigning a safety ranking to road segments on five state roads. This product can assist traffic engineers in decision-making to improve road safety by prioritizing projects that address problematic locations. The results show a promising approach to enhancing road safety and preventing crashes.
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