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Event-Driven Safe and Resilient Control of Automated and Human-Driven Vehicles under EU-FDI Attacks

Yi Zhang, Yichao Wang, Wei Xiao, Mohamadamin Rajabinezhad, Shan Zuo

TL;DR

Simulation results validate the effectiveness and robustness of the proposed EDSR framework in achieving collision-free maneuvers, stable velocity regulation, and resilient operation under adversarial conditions.

Abstract

This paper studies the safe and resilient control of Connected and Automated Vehicles (CAVs) operating in mixed traffic environments where they must interact with Human-Driven Vehicles (HDVs) under uncertain dynamics and exponentially unbounded false data injection (EU-FDI) attacks. These attacks pose serious threats to safety-critical applications. While resilient control strategies can mitigate adversarial effects, they often overlook collision avoidance requirements. Conversely, safety-focused approaches tend to assume nominal operating conditions and lack resilience to adversarial inputs. To address these challenges, we propose a control framework that integrates event-driven Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs) with adaptive attack-resilient control. The framework further incorporates data-driven estimation of HDV behaviors to ensure safety and resilience against EU-FDI attacks. Specifically, we focus on the lane-changing maneuver of CAVs in the presence of unpredictable HDVs and EU-FDI attacks on acceleration inputs. The event-driven approach reduces computational load while maintaining real-time safety guarantees. Simulation results, including comparisons with pure event-driven methods lacking resilience, validate the effectiveness and robustness of the proposed EDSR framework in achieving collision-free maneuvers, stable velocity regulation, and resilient operation under adversarial conditions.

Event-Driven Safe and Resilient Control of Automated and Human-Driven Vehicles under EU-FDI Attacks

TL;DR

Simulation results validate the effectiveness and robustness of the proposed EDSR framework in achieving collision-free maneuvers, stable velocity regulation, and resilient operation under adversarial conditions.

Abstract

This paper studies the safe and resilient control of Connected and Automated Vehicles (CAVs) operating in mixed traffic environments where they must interact with Human-Driven Vehicles (HDVs) under uncertain dynamics and exponentially unbounded false data injection (EU-FDI) attacks. These attacks pose serious threats to safety-critical applications. While resilient control strategies can mitigate adversarial effects, they often overlook collision avoidance requirements. Conversely, safety-focused approaches tend to assume nominal operating conditions and lack resilience to adversarial inputs. To address these challenges, we propose a control framework that integrates event-driven Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs) with adaptive attack-resilient control. The framework further incorporates data-driven estimation of HDV behaviors to ensure safety and resilience against EU-FDI attacks. Specifically, we focus on the lane-changing maneuver of CAVs in the presence of unpredictable HDVs and EU-FDI attacks on acceleration inputs. The event-driven approach reduces computational load while maintaining real-time safety guarantees. Simulation results, including comparisons with pure event-driven methods lacking resilience, validate the effectiveness and robustness of the proposed EDSR framework in achieving collision-free maneuvers, stable velocity regulation, and resilient operation under adversarial conditions.
Paper Structure (9 sections, 3 theorems, 25 equations, 11 figures)

This paper contains 9 sections, 3 theorems, 25 equations, 11 figures.

Table of Contents

  1. INTRODUCTION
  2. PROBLEM FORMULATION
  3. HUMAN-IN-THE-LOOP SAFE LANE-CHANGING MANEUVER
  4. Event-Driven Safety Control
  5. Attack-Resilient Control
  6. SIMULATION RESULTS
  7. Performance of the Proposed EDSR Framework under EU-FDI
  8. Comparative Study with Event-Driven CBF in xiao2022event
  9. Conclusion

Key Result

Lemma 1

Let $b(\boldsymbol{x}(t))$ be a continuously differentiable function representing a state constraint such that the safe set is defined as $\mathcal{C} = \{ \boldsymbol{x}(t) \in \mathbb{R}^n \mid b(\boldsymbol{x}(t)) \geq 0 \}$. Assume that $L_g b(\boldsymbol{x}(t)) \neq 0$ when $b(\boldsymbol{x}(t) where $L_f(\cdot)$ and $L_g(\cdot)$ denote the Lie derivatives of $b(\boldsymbol{x}(t))$ along the

Figures (11)

  • Figure 1: Illustration of a basic lane-changing maneuver: the yellow vehicle is an HDV, green vehicles are CAVs, and the grey vehicle is a slow-moving, uncontrollable vehicle.
  • Figure 2: Lane-changing maneuver under EU-FDI attacks using the proposed EDSR framework.
  • Figure 3: EU-FDI Attack Signal.
  • Figure 4: Lateral position and speed under EU-FDI attacks using the proposed EDSR control framework.
  • Figure 5: Safety performance under EU-FDI attacks using the proposed EDSR control framework.
  • ...and 6 more figures

Theorems & Definitions (9)

  • Definition 1: khalil2002nonlinear
  • Remark 1
  • Lemma 1: xiao2022event
  • Remark 2
  • Lemma 2: li2024safe
  • Remark 3
  • Remark 4
  • Theorem 1
  • proof