Theorizing Connected Vehicle-Enabled Traffic System Vulnerability Analysis with positioning, navigation, and timing (PNT)

This project investigates the cybersecurity vulnerabilities of connected autonomous vehicles (CAVs) with a focus on positioning, navigation, and timing (PNT) disruptions. This study examines how cyber-attacks, including false messaging, acceleration manipulation, and platoon leader identity attacks, impact traffic flow and system stability. By integrating an extended Intelligent Driver Model (IDM) with machine learning techniques such as Random Forest and stacked LSTM, the research develops an anomaly detection framework capable of identifying cyber-induced disruptions in real time. Simulation results demonstrate that even short-duration cyber-attacks can lead to significant travel delays, traffic instability, and collisions. The proposed methodology enhances cybersecurity resilience in connected transportation systems, aligning with USDOT's goals of improving safety, mobility, and infrastructure protection. These findings contribute to developing robust cybersecurity strategies for emerging autonomous vehicle networks

• Record URL:
  • https://uh.edu/cybercare/research/projects/c23c02/
• Supplemental Notes:
  • This material is based on work supported by the U.S. Department of Transportation, OST-R, University Transportation Center Program, the USDOT Tier 1 UTC Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE).

Language

• English

Project

• Status: Active
• Funding: $60,000.00
• Contract Numbers:

  69A3552348332

• 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:

  Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE)

  University of Houston
  Houston, TX  United States 
• Project Managers:

  Zhang, Yunpeng

• Performing Organizations:

  University of Houston, Texas

  Houston, TX  United States 
• Principal Investigators:

  Gao, Lu

• Start Date: 20230701
• Expected Completion Date: 20260630
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Computer security; Connected vehicles; Detection and identification systems; Drivers; Machine learning; Risk analysis; Traffic simulation
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Safety and Human Factors; Security and Emergencies; Vehicles and Equipment;

Filing Info

• Accession Number: 01953959
• Record Type: Research project
• Source Agency: Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE)
• Contract Numbers: 69A3552348332
• Files: UTC, RIP, STATEDOT
• Created Date: Apr 30 2025 4:12PM