Road-side based cybersecurity in connected and automated vehicle systems

The objective of this research project is to further the knowledge on cybersecurity in connected and automated vehicles (CAVs). Specifically, we aim to develop a holistic framework that integrates physics-based data-driven modeling and dynamic decision making under uncertainty and partial information to improve cybersecurity in CAVs. CAVs are anticipated to enhance our current transportation system in terms of safety and mobility, and curb the environmental implications of the transportation sector. Despite these benefits, major concerns remain as to whether an interconnected network of CAVs and infrastructure is vulnerable to malicious hackers or unintentional faults. In this proposed work, we aim to address open questions on cybersecurity of a network of connected CAVs. Our goal is to develop an integrated real-time, robust, and scalable context-aware framework to ensure safe navigation of CAVs and other road users. We will validate the framework using existing data from ongoing pilot studies as well as new simulated data which will be produced as part of this proposed work. The proposed framework contributes to the literature of anomaly detection/identification, data fusion, non-linear control, physics-based learning, and decision making under uncertainty in novel and important ways. It will build on the state-of-the-art filters, control algorithms, and machine learning methods to address scientific challenges with respect to incorporating `context' to improve learning and decision making under adversarial conditions. This context includes a vehicle's motion in relationship with its surrounding traffic, which is complicated by the stochastic time delay in receiving basic safety messages from the connected vehicles/infrastructure or in collecting and contextualizing data by the vehicle's on-board sensors.

  • Record URL:
  • Supplemental Notes:
    • This project will adopt a two-level implementation plan. First, we will use an existing cybersecurity dataset to assess the performance of the developed cybersecurity solutions. Next, we will use an in-house simulation platform that is capable of modeling connected and automated vehicles in isolation and in platoons to assess the performance of our solutions on edge cases and scenarios for which naturalistic driving data does not exist.


  • English


  • Status: Completed
  • Funding: $153,764
  • Contract Numbers:


  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

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

    Center for Connected and Automated Transportation

    University of Michigan Transportation Research Institute
    Ann Arbor, MI  United States  48109
  • Managing Organizations:

    Center for Connected and Automated Transportation

    University of Michigan Transportation Research Institute
    Ann Arbor, MI  United States  48109

    University of Michigan Transportation Research Institute

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

    Tucker-Thomas, Dawn

    Bezzina, Debra

  • Performing Organizations:

    University of Michigan, Ann Arbor

    Department of Civil and Environmental Engineering
    2350 Hayward
    Ann Arbor, MI  United States  48109-2125
  • Principal Investigators:

    Masoud, Neda

    Liu, Henry

  • Start Date: 20210101
  • Expected Completion Date: 20221231
  • Actual Completion Date: 20230808
  • USDOT Program: University Transportation Centers Program
  • Subprogram: Research

Subject/Index Terms

Filing Info

  • Accession Number: 01768635
  • Record Type: Research project
  • Source Agency: Center for Connected and Automated Transportation
  • Contract Numbers: 69A3551747105
  • Files: UTC, RIP
  • Created Date: Mar 26 2021 6:08PM