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

This project seeks to uncover, characterize, and mitigate the biases of AI models in connected vehicle-infrastructure-pedestrian systems by leveraging advanced statistical machine learning and representation learning techniques and real-world video data Pedestrian safety remains a critical challenge in current transportation systems. In the US, fatal pedestrian crashes have increased by nearly 50% over the past decade. Data shows that children, the elderly, men, and people with low income are involved in far greater pedestrian-vehicle crashes compared to the general population. Autonomous Vehicles (AVs) are expected to effectively detect pedestrians and react to potential accidents. However, due to the constrained mobilities of vulnerable road users, data from vulnerable pedestrians, such as the elderly and children, is often limited. For example, children are more likely to exhibit unpredictable behaviors, and elderly pedestrians on average walk slower than the general population, as shown conceptually in Figure 1. The data scarcity and distinct distributions of these pedestrian groups will make their data minor ``mode" or even ``out-of-distribution" compared to the huge amount of training data from other pedestrian groups. This will lead to larger prediction errors for those groups during the testing stage. Error-prone detection and trajectory prediction of vulnerable pedestrians may cause decision-making that compromises their safety.

Language

• English

Project

• Status: Completed
• 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: 20241231
• Actual Completion Date: 20250131
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

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

Filing Info

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