Reasoning and Mitigating AI Biases in Connected Vehicle-Infrastructure-Pedestrian Systems for Promoting Equitable Pedestrian Safety at Intersections

Pedestrian safety remains a critical challenge in current transportation systems. In the United States (US), fatal pedestrian crashes have increased nearly 50% over the past decade. Data shows that children, older adults, men, people with low income, people experiencing homelessness, and people of color are involved in far greater pedestrian-vehicle crashes compared to the general population. Automated Vehicles (AVs) are expected to effectively detect pedestrians and react to potential crashes. However, due to the constrained mobilities of vulnerable road users, data from vulnerable pedestrians, such as older adults and children, is often limited. This project seeks to uncover, characterize, and mitigate the biases of artificial intelligence (AI) models in connected vehicle-infrastructure-pedestrian systems by leveraging advanced statistical machine learning, representation learning techniques, and real-world video data. The discovery results of the biases of AI models and proposed bias mitigation solutions in connected vehicle-infrastructure-pedestrian systems will help promote the transformation design of future intelligent transportation systems, ensuring the travel safety and justice of vulnerable road users, children, and older adults under such transformations. The project will benefit the design of future management policy and standards by providing a novel and systematic AI bias evaluation framework for general connected vehicle-infrastructure pedestrian systems. Keywords: REAT, Automated Vehicles (AVs),

Language

• English

Project

• Status: Active
• Funding: $Federal: $97,106 Matching: $52,770
• 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:

  Stony Brook University

  100 Nicolls Road
  Stony Brook, NY  United States  11794
• Principal Investigators:

  Xu, Susu

• Start Date: 20230601
• Expected Completion Date: 20240531
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Artificial intelligence; Connected vehicles; Machine learning; Pedestrian safety; Video; Vulnerable road users
• Subject Areas: Highways; Pedestrians and Bicyclists; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01896748
• Record Type: Research project
• Source Agency: Rural Equitable and Accessible Transportation Center
• Contract Numbers: 69A3552348321
• Files: UTC, RIP
• Created Date: Oct 19 2023 4:46PM