Safe and Reliable Autonomous Vehicle Navigation through Cyber Resilience

The reliable operation of Autonomous Vehicles (AVs) hinges on robust and reliable Positioning, Navigation, and Timing (PNT) services, predominantly provided by Global Navigation Satellite Systems (GNSS). The U.S.-owned Global Positioning System (GPS) consists of Ground Control Stations (GCS), Space Vehicles (SV), and user segment receivers, all of which could be susceptible to natural interferences and cyber threats. GCS, vulnerable to physical and cyberattacks, can transmit compromised correction data to satellites, posing significant risks to navigation integrity. GNSS signals are inherently weak and susceptible to unintentional interference, such as signal blocking, urban canyon multipath, and atmospheric effects, as well as deliberate threats like jamming and spoofing, which significantly amplify uncertainties in PNT services. Although alternative PNT solutions, including Low Earth Orbit (LEO) satellites, Wi-Fi, and cellular-based technologies, show promise, they remain limited in coverage, underdeveloped, and/or vulnerable to intentional interference. High-definition (HD) map-based navigation systems are also at risk of exploitation by hackers. Multi-sensor fusion systems, integrating GNSS with inertial measurement units (IMU) and perception sensors (PS), such as cameras, LiDAR, and RADAR, offer potential solutions by complementing individual sensor outputs in contested environments. However, IMUs suffer from error accumulation, and PS performance is compromised by limited line-of-sight or adverse weather conditions (e.g., snow and heavy rain), which degrade positioning accuracy. To overcome these challenges, the overarching goal of this project is to enhance the security of GNSS-based navigation systems through four key objectives: (1) identifying and analyzing vulnerabilities in GNSS ground control and user segments to develop intelligent cyber-attack models, (2) designing and implementing sensor fusion algorithms that leverage loosely coupled GNSS, IMU, and perception sensor data for the detection of GNSS cyber-attacks, (3) developing advanced mitigation strategies to counter spoofing attacks and restore authentic GNSS signal lock, and (4) deploying these detection and mitigation algorithms in secured execution environments (TEEs) to safeguard operational integrity against software-based threats. By addressing GNSS vulnerabilities, the research will significantly enhance the safety and reliability of GNSS-based navigation for autonomous vehicles, foster public and industry reliability in these technologies, and support broader advancements in transportation cybersecurity.

• Record URL:
  • https://www.clemson.edu/cecas/tracr/research/projects/#safeandreliableautonomousvehiclenavigationthroughcyberresilience

Language

• English

Project

• Status: Active
• Funding: $318,817.00
• Contract Numbers:

  69A3552344812

  69A3552348317

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590

  University of Alabama, Tuscaloosa

  Department of Civil, Construction and Environmental Engineering
  P.O. Box 870205
  Tuscaloosa, AL  United States  35487-0205

  Clemson University

  216 Lowry Hall
  Clemson, SC, SC  United States  29634
• Managing Organizations:

  National Center for Transportation Cybersecurity and Resiliency (TraCR)

  Clemson University
  Clemson, SC  United States 

  University of Alabama, Tuscaloosa

  Department of Civil, Construction and Environmental Engineering
  P.O. Box 870205
  Tuscaloosa, AL  United States  35487-0205
• Project Managers:

  Chowdhury, Mashrur

• Performing Organizations:

  University of Alabama, Tuscaloosa

  Department of Civil, Construction and Environmental Engineering
  P.O. Box 870205
  Tuscaloosa, AL  United States  35487-0205

  Clemson University

  216 Lowry Hall
  Clemson, SC, SC  United States  29634
• Principal Investigators:

  Rahman, Mizanur

  Dasgupta, Sagar

  Cheng, Long

  Chowdhury, Mashrur

• Start Date: 20250101
• Expected Completion Date: 20251231
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Algorithms; Autonomous vehicles; Computer security; Countermeasures; Data fusion; Detection and identification technologies; Risk assessment; Sensors
• Identifier Terms: Global Navigation Satellite System
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Security and Emergencies; Vehicles and Equipment;

Filing Info

• Accession Number: 01950450
• Record Type: Research project
• Source Agency: National Center for Transportation Cybersecurity and Resiliency (TraCR)
• Contract Numbers: 69A3552344812, 69A3552348317
• Files: UTC, RIP
• Created Date: Mar 31 2025 5:16PM