Holistically Identifying Road Complexity and Relating it to Fatal Crashes
Understanding the context of crash occurrence in complex driving environments is essential for improving traffic safety and advancing automated driving. Previous studies have used statistical models and deep learning to predict crashes based on semantic, contextual, or vehicle kinematic features, but none have examined the combined influence of these factors. In this study, we term the integration of these features ``roadway complexity''. This paper introduces a two-stage framework that integrates roadway complexity features for crash prediction. In the first stage, an encoder extracts hidden contextual information from these features, generating latent complexity features. The second stage uses both original and latent complexity features to predict crash likelihood, achieving an accuracy of 87.98% with original features alone and 90.46% with the added latent complexity features. Ablation studies confirm that a combination of semantic, kinematic, and contextual features yields the best results, which emphasize their role in capturing roadway complexity. Additionally, complexity index annotations generated by the Large Language Model outperform those by Amazon Mechanical Turk, highlighting the potential of AI-based tools for accurate, scalable crash prediction systems.
- Record URL:
Language
- English
Project
- Status: Completed
- Funding: $120000
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Contract Numbers:
69A3552348301
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Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
University of Massachusetts, Amherst
Department of Civil and Environmental Engineering
130 Natural Resources Road
Amherst, MA United States 01003 -
Performing Organizations:
University of Massachusetts, Amherst
Department of Civil and Environmental Engineering
130 Natural Resources Road
Amherst, MA United States 01003 -
Principal Investigators:
Roberts, Shannon
- Start Date: 20240101
- Expected Completion Date: 20241231
- Actual Completion Date: 20250131
- USDOT Program: University Transportation Centers Program
- Subprogram: University Transportation Centers
Subject/Index Terms
- TRT Terms: Autonomous vehicles; Computer vision; Fatalities; High risk locations; Highway factors in crashes; Traffic crashes; Traffic safety; Vehicle safety
- Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;
Filing Info
- Accession Number: 01905080
- Record Type: Research project
- Source Agency: New England University Transportation Center
- Contract Numbers: 69A3552348301
- Files: UTC, RIP
- Created Date: Jan 19 2024 10:22AM