Improving Security and User Privacy in Learning-Based Traffic Signal Controllers (TSC)

The 21st century of transportation systems leverages intelligent learning agents and data-centric approaches to analyze information gathered with sensing (both vehicles & road sides) or shared by users to improve transportation efficiency and safety. Numerous machine learning (ML) models have been incorporated to make control decisions (e.g., traffic light control schedules) based on mining mobility data sets and real-time input from vehicles via vehicle-to-vehicle and vehicle-to-infrastructure communications. However, in such situations, where ML models are used for automation by leveraging external inputs, the associated security and privacy issues start to surface. This project aims to study the security of machine learning systems and data privacy associated with learning-based traffic signal controllers (TSCs). The preliminary work has demonstrated that deep reinforcement learning (DRL) based TSCs are vulnerable to both white-box and black-box cyber-attacks. The research goals include (1) quantifying the impact of such security vulnerabilities on the safety and efficiency of the TSC operation, and (2) developing effective detection and mitigation mechanisms for such attacks. In learning based TSCs, vehicles share their messages with the DRL agents at TSCs, which will then analyze the data and take action. Sharing vehicular mobility data with a network of TSCs may cause privacy leakage. To address this problem, the research team proposes to apply differential privacy techniques to the mobility datasets to protect user privacy while preserving the effectiveness of the prediction outcomes of traffic-actuated or learning-based TSC algorithms. The team will evaluate their approaches in vehicular simulator using real mobility data from San Francisco and other cities in California. By accomplishing these goals, learning-based transportation systems will be more secure and reliable for real-time implementations.

Language

  • English

Project

  • Status: Programmed
  • Funding: $71357
  • Contract Numbers:

    69A3551747119

  • 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:

    USDOT/OST-R

    Washington DC,   United States 
  • Project Managers:

    Kline, Robin

  • Performing Organizations:

    University of California, Davis

    1 Shields Ave
    Davis, California  United States  95616
  • Principal Investigators:

    Chuah, Chen-Nee

  • Start Date: 20210401
  • Expected Completion Date: 20220331
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01744113
  • Record Type: Research project
  • Source Agency: Center for Transportation, Environment, and Community Health
  • Contract Numbers: 69A3551747119
  • Files: UTC, RiP
  • Created Date: Jun 25 2020 12:42PM