Secure Multi-Modal Transportation Artificial Intelligence (AI) at Run-Time

This research develops robust and secure artificial intelligence (AI) systems for smart transportation that could defend against novel adversarial attacks with high performance. The project addresses critical vulnerabilities in Test-Time Adaptation (TTA) mechanisms and Multimodal Large Language Models deployed in transportation systems. Through new attack discovery, effective defense framework development, and deployable prototype systems, this work ensures that AI technologies can be safely deployed in safety-critical transportation applications. The research delivers practical solutions including natural scene adversarial attack frameworks, sharpness-aware minimization based TTA defenses, event-conditioned representation compression for efficient multimodal AI, and adversarially robust multimodal fusion architectures.

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

Language

• English

Project

• Status: Active
• Funding: $243,670.00
• Contract Numbers:

  69A3552344812

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

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

  Clemson University

  216 Lowry Hall
  Clemson, SC, SC  United States  29634

  University of Texas at Dallas

  800 W Campbell Rd
  Richardson, Texas  United States  75080
• Managing Organizations:

  National Center for Transportation Cybersecurity and Resiliency (TraCR)

  Clemson University
  Clemson, SC  United States 

  Clemson University

  216 Lowry Hall
  Clemson, SC, SC  United States  29634
• Project Managers:

  Chowdhury, Mashrur

• Performing Organizations:

  Clemson University

  216 Lowry Hall
  Clemson, SC, SC  United States  29634

  University of Texas at Dallas

  800 W Campbell Rd
  Richardson, Texas  United States  75080
• Principal Investigators:

  Wu , Yongkai

  Luo, Feng

  Khan, Latifur

  Thuraisingham, Bhavani

  Salek, Sabbir

• Start Date: 20260401
• Expected Completion Date: 20270331
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Artificial intelligence; Autonomous vehicles; Computer security; Cyberattacks; Information processing; Multimodal transportation; Traffic safety; Vehicle safety
• Subject Areas: Data and Information Technology; Highways; Pedestrians and Bicyclists; Safety and Human Factors; Security and Emergencies; Vehicles and Equipment;

Filing Info

• Accession Number: 01988090
• Record Type: Research project
• Source Agency: National Center for Transportation Cybersecurity and Resiliency (TraCR)
• Contract Numbers: 69A3552344812
• Files: UTC, RIP
• Created Date: Apr 28 2026 3:43PM