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Secure Control of Connected and Automated Vehicles Using Trust-Aware Robust Event-Triggered Control Barrier Functions

H M Sabbir Ahmad, Ehsan Sabouni, Akua Dickson, Wei Xiao, Christos G. Cassandras, Wenchao Li

TL;DR

This work tackles secure, decentralized coordination of CAVs at conflict zones under V2X and sensing uncertainties and adversarial threats. It introduces a trust-aware robust event-triggered control framework based on Control Barrier Functions, where per-vehicle trust modulates safety constraints and an attack mitigation strategy preserves safety while restoring normal traffic flow. The approach provides formal forward-invariance guarantees and demonstrates improved throughput and energy performance in SUMO and CARLA simulations, along with resilience to Sybil and Bias Injection attacks and protection against false positives. The framework is designed to be largely agnostic to the specific trust implementation and can be complemented by standard cryptographic measures for defense-in-depth, offering a scalable path to safer real-world CAV networks.

Abstract

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.

Secure Control of Connected and Automated Vehicles Using Trust-Aware Robust Event-Triggered Control Barrier Functions

TL;DR

This work tackles secure, decentralized coordination of CAVs at conflict zones under V2X and sensing uncertainties and adversarial threats. It introduces a trust-aware robust event-triggered control framework based on Control Barrier Functions, where per-vehicle trust modulates safety constraints and an attack mitigation strategy preserves safety while restoring normal traffic flow. The approach provides formal forward-invariance guarantees and demonstrates improved throughput and energy performance in SUMO and CARLA simulations, along with resilience to Sybil and Bias Injection attacks and protection against false positives. The framework is designed to be largely agnostic to the specific trust implementation and can be complemented by standard cryptographic measures for defense-in-depth, offering a scalable path to safer real-world CAV networks.

Abstract

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.
Paper Structure (16 sections, 5 theorems, 34 equations, 5 figures, 1 table)

This paper contains 16 sections, 5 theorems, 34 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Constraints/rules in the Control Zone
  4. Decentralized control formulation:
  5. Trust framework
  6. Threat model
  7. Safe and Resilient Control Formulation using Trust Aware CBFs
  8. Trust-aware coordination
  9. Trust-Aware CBFs
  10. Robust Trust-Aware CBFs
  11. Event-triggered Control
  12. Attack Detection and Mitigation
  13. Determination of Fake CAVs
  14. Robust Mitigation
  15. Simulation Results
  16. ...and 1 more sections

Key Result

Lemma 1

Given a constraint $b_q(\boldsymbol{x}(t))$ associated with the set $\mathrm{C}:=\{\boldsymbol{x}\in \mathbb{R}^n:b_q(\boldsymbol{x})\geq 0\}$ and $\|\boldsymbol{w}_{i,j}\|_{\infty} \leq \epsilon_1$, any Lipschitz continuous controller $u(t)$ that satisfies renders the set $C$ forward invariant $\forall t \geq t_{0}$ for the system VehicleDynamics.

Figures (5)

  • Figure 1: The multi-lane intersection problem. Collisions may happen at the MPs (red dots shown in above figure).
  • Figure 2: Results illustrating the merit of our proposed robust trust-aware event-triggered control scheme. The result was generated by simulating an attack scenario combining BI attack with Sybil attack. As can be seen, the framework in ahmad_02 results in safety violation (left) which is prevented by our proposed robust trust-aware event- triggered control scheme. The images shown above are from CARLA simulations.
  • Figure 3: The values of average travel time, average energy, and average fuel consumption for real CAVs for different proportions of fake CAVs over 5 runs with and without our proposed mitigation scheme.
  • Figure 4: The figure shows the performance of the network during a Sybil attack containing six spoofed CAVs without (left) and with (right) our proposed attack mitigation scheme. The picture was taken after 1 minute of running the simulation. The spoofed CAVs were located in three of the eight lanes.
  • Figure 5: Percentage of safe scenarios over 100 runs for different degrees of accuracy of the onboard vision system.

Theorems & Definitions (14)

  • Definition 1
  • Definition 2
  • Lemma 1
  • proof
  • Theorem 1
  • proof
  • Corollary 1
  • proof
  • Corollary 2
  • proof
  • ...and 4 more