Development of Safety Performance Function Based on the Vehicle Automation Levels

Vehicle automation improves highway safety by integrating advanced driving assistance systems (ADAS) into modern vehicles. These systems, such as automatic braking, lane keeping, and adaptive cruise control, not only prevent collisions but also contribute to a significant reduction in fatality rates on highway systems. Traditionally, transportation agencies and researchers have relied on safety performance functions (SPFs) and crash modification factors (CMFs) to identify high-risk road segments and establish the benefit of deploying a particular countermeasure. These analytical approaches are essential for network screening and safety analysis, as they quantify the relationship between roadway characteristics, traffic volume, and crash occurrences. Despite these advancements, studies calibrating SPFs for various ADAS technologies to assess the safety potential of ADAS technologies in terms of crash frequency and injury severity remain limited. To address this gap, the current research aims to develop SPFs and CMFs for various ADAS technologies. As a result, this work seeks to enhance our understanding of how different automated systems contribute to roadway safety and provide more accurate tools for network screening and targeted safety interventions.

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 Safe, Efficient, and Advanced Transportation Center
• Contract Numbers: 69A3552348321
• Files: UTC, RIP
• Created Date: Feb 12 2025 5:23PM