Holistically Identifying Road Complexity and Relating it to Fatal Crashes

This project addresses the critical issue of traffic fatalities, which have reached a 16-year high, by focusing on the identification of complex roadways and their impact on fatal crashes. Utilizing the MIT-AVT dataset, which includes extensive driving data from various vehicles equipped with advanced technologies, the research employs a computer vision model with panoptic segmentation to detect multiple objects in road scenes, thereby determining roadway complexity. The project aims to analyze this complexity in relation to driver behavior and the occurrence of fatal crashes. The research is divided into three phases: feature extraction, data integration, and model building. Feature extraction involves using the OneFormer panoptic segmentation model to label specific regions of images, while data integration involves annotating these images to create a road complexity dataset. The final phase includes building classification models to identify fatal crash hot spots and conducting correlation analysis to explore the relationship between road complexity, driver behavior, and fatal crashes. This research is significant for its potential to enhance automated vehicle safety and inform traffic safety policy.

• Record URL:
  https://www.umass.edu/neutc/projects/holistically-identifying-road-complexity-and-relating-it-fatal-crashes

Language

• English

Project

• Status: Active
• Funding: $120000
• Contract Numbers:

  69A3552348301

• 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: 0
• 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