Development of Safety Performance Function Based on the Vehicle Automation Levels

The emergence of Advanced Driver Assistance Systems (ADAS) has revolutionized transportation by enhancing safety on highways. These systems have become more vital in preventing crashes and significantly reducing the fatality rate on highways. However, a few ADAS-equipped vehicles are operating on highways compared to conventional vehicles. Due to the presence of significant disproportionality between ADAS-equipped vehicles and other conventional vehicles, the benefits of ADAS systems are overstated when it comes to crash prediction. The crash prediction of a location tends to use safety performance functions (SPF) that suitably address the criteria of an area. However, during the development of the SPF, the crashes from both the conventional vehicles and the ADAS equipped were mixed or some SPF that are currently used were developed a long time ago that did not account for the ADAS equipped vehicle. Therefore, ensuring that the benefits of the ADAS systems are not overlooked during crash prediction there is a need for developing new SPFs that account specifically for the ADAS-equipped vehicle. Studies have been conducted to develop SPF for different conductions and areas utilizing different optimizing methodologies. However, little or no effort has been made to develop a safety performance function that accounts for the ADAS-equipped vehicles hence there is vague/not accurate capability of predicting the number of crashes that involve ADAS-equipped vehicles.

Language

• English

Project

• Status: Active
• Funding: $96,694.00
• Contract Numbers:

  69A3552348321

• 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:

  Florida A&M University, Tallahassee

  404 Foote/Hilyer
  Tallahassee, FL  United States  32307
• Project Managers:

  Moses, Ren

• Performing Organizations:

  Cleveland State University

  Euclid Avenue at 24th Street
  Cleveland, Oh  United States  44115
• Principal Investigators:

  Kidando, Emmanuel

  Owusu-Danquah, J

• Start Date: 20241001
• Expected Completion Date: 20251230
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Crash risk forecasting; Driver support systems; Predictive models; Traffic safety
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01945633
• Record Type: Research project
• Source Agency: Rural Equitable and Accessible Transportation Center
• Contract Numbers: 69A3552348321
• Files: UTC, RIP
• Created Date: Feb 12 2025 5:23PM