Intelligent Safety Assessment of Rural Roadways Using Automated Image and Video Analysis [Research Brief]

Markovic, Nikola; Rashidi, Abbas; Mashhadi, Ali Hassandokht

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Intelligent Safety Assessment of Rural Roadways Using Automated Image and Video Analysis [Research Brief]

2023-12-01
By Markovic, Nikola ; Rashidi, Abbas ; Mashhadi, Ali Hassandokht

File Language:

English

Details

Creators:

Markovic, Nikola ; Rashidi, Abbas ; Mashhadi, Ali Hassandokht
Corporate Creators:

University of Utah ; Mountain-Plains Consortium
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
Subject/TRT Terms:
Publication/ Report Number:

MPC 23-510
Resource Type:

Brief
Geographical Coverage:

United States ; Utah
Edition:

Research Brief
Corporate Publisher:

Mountain-Plains Consortium
Abstract:

This project introduces an innovative solution employing computer vision and machine learning algorithms to automate the roadside safety evaluation process. Utilizing pre-trained models such as VGG16 and images captured from Utah roadways, the research team develops a robust algorithm for automated safety evaluation that aligns with the FHWA rating system, providing a comprehensive and efficient method for assessing roadside conditions. Tailored computer vision algorithms detect specific features, enhancing the accuracy of safety evaluations. Pre-trained models for clear zone detection and roadside slope classification further contribute to a nuanced understanding of roadside elements. The methodology employs various computer vision algorithms to automatically rank roadside safety by detecting features such as guardrails, clear zones, rigid obstacles, and roadside slopes.
Format:

PDF
Funding:

669
Download URL:

https://rosap.ntl.bts.gov/view/dot/73729/dot_73729_DS1.pdf
File Type:

[PDF - 704.71 KB]
Collection(s):

Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers
Main Document Checksum:

urn:sha-512:dc5f9dc67492476f74bc2f4b8b9c4c7dcf676761757bb2839f686207d7612c9576bc0af16134f646cba020c4233f8e0f869d500ca86d296465e67f6e64ff5472

File Language:

English

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