Hardening the CAV Ecosystem to Reduce Cybersecurity Risks – Year One

In this project, the study team takes a multi-pronged framework to secure the CAV ecosystem by simultaneously considering cybersecurity threats posed to CAVs, transportation infrastructure, and cyber infrastructure that support CAV operations and leveraging intelligent collaborations between CAVs and physical/cyber transportation infrastructures. The project team will investigate how to exploit cooperative perception and advanced communication technologies - especially emerging 5G/Next-Generation (NextG) networking and computing capabilities and combine them with innovative artificial intelligence and machine learning (AI/ML) algorithms to secure the CAV ecosystem through a list of research tasks including developing a secure and robust perception system, investigating the security of cooperative driving and communication between CAVs and infrastructure, and designing a secured CAV system through redundancy.

• Record URL:
  https://ccat.umtri.umich.edu/research/minnesota/hardening-the-cav-ecosystem-to-reduce-cybersecurity-risks-year-one/

Language

• English

Project

• Status: Active
• Funding: $625000
• Contract Numbers:

  69A3552348305

• 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:

  University of Michigan Transportation Research Institute

  2901 Baxter Road
  Ann Arbor, Michigan  United States  48109
• Project Managers:

  Stearns, Amy

• Performing Organizations:

  University of Michigan, Ann Arbor

  North Campus
  Ann Arbor, MI  United States  48109

  Purdue University, Lyles School of Civil Engineering

  550 Stadium Mall Drive
  West Lafayette, IN  United States  47907

  University of Minnesota, Minneapolis

  Center for Transportation Studies
  Minneapolis, MN  United States 
• Principal Investigators:

  Mao, Z. Morley

  Feng, Yiheng

  Zhang, Zhi-Li

• Start Date: 20231001
• Expected Completion Date: 20250930
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Artificial intelligence; Autonomous vehicles; Computer security; Connected vehicles; Infrastructure; Machine learning; Mobile communication systems; Risk assessment
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01906130
• Record Type: Research project
• Source Agency: Center for Connected and Automated Transportation
• Contract Numbers: 69A3552348305
• Files: UTC, RIP
• Created Date: Jan 26 2024 4:47PM