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The combination of connectivity and automation allows connected and autonomous vehicles (CAVs) to operate autonomously using advanced on-board sensors while communicating with each other via vehicle-to-vehicle (V2V) technology to enhance safety, efficiency, and mobility. One of the most promising features of CAVs is cooperative adaptive cruise control (CACC). This system extends the capabilities of conventional adaptive cruise control (ACC) by facilitating the exchange of critical parameters among vehicles to enhance safety, traffic flow, and efficiency. However, increased connectivity introduces new vulnerabilities, making CACC susceptible to cyber-attacks, including false data injection (FDI) attacks, which can compromise vehicle safety. To address this challenge, we propose a secure observer-based control design leveraging Lyapunov stability analysis, which is capable of mitigating the adverse impact of FDI attacks and ensuring system safety. This approach uniquely addresses system security without relying on a known lead vehicle model. The developed approach is validated through simulation results, demonstrating its effectiveness.
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Secure Control of Connected and Automated Vehicles Using Trust-Aware Robust Event-Triggered Control Barrier Functions
We address the security of a network of Connected and Automated Vehicles (CAVs) cooperating to safely navigate through a conflict area (e.g., traffic intersections, merging roadways, roundabouts). Previous studies have shown that such a network can be targeted by adversarial attacks causing traffic jams or safety violations ending in collisions. We focus on attacks targeting the V2X communication network used to share vehicle data and consider as well uncertainties due to noise in sensor measurements and communication channels. To combat these, motivated by recent work on the safe control of CAVs, we propose a trust-aware robust event-triggered decentralized control and coordination framework that can provably guarantee safety. We maintain a trust metric for each vehicle in the network computed based on their behavior and used to balance the tradeoff between conservativeness (when deeming every vehicle as untrustworthy) and guaranteed safety and security. It is important to highlight that our framework is invariant to the specific choice of the trust framework. Based on this framework, we propose an attack detection and mitigation scheme which has twofold benefits: (i) the trust framework is immune to false positives, and (ii) it provably guarantees safety against false positive cases. We use extensive simulations (in SUMO and CARLA) to validate the theoretical guarantees and demonstrate the efficacy of our proposed scheme to detect and mitigate adversarial attacks.
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- Award ID(s):
- 1932162
- PAR ID:
- 10553380
- Publisher / Repository:
- Symposium on Vehicles Security and Privacy (VehicleSec) 2024
- Date Published:
- ISBN:
- 979-8-9894372-7-6
- Format(s):
- Medium: X
- Location:
- https://www.ndss-symposium.org/wp-content/uploads/vehiclesec2024-37-paper.pdf
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
