Cyberattacks on Adaptive Cruise Control Vehicles: An Analytical Characterization

TL;DR

This work addresses the vulnerability of adaptive cruise control (ACC) vehicles to cyberattacks in mixed traffic, proposing a general analytical framework to synthesize two attack classes with explicit adverse effects on traffic safety and efficiency.It introduces a linear stability analysis for car-following dynamics and derives conditions that characterize the malicious nature of Type I (control-command) and Type II (sensor-data) attacks, using bounded, state-dependent attack signals to reflect realistic adversaries.The authors provide analytical criteria for attack synthesis and conduct extensive simulations on a ring-road to reveal how strategically designed attacks disrupt microscopic car-following and macroscopic fundamental diagrams, highlighting trade-offs between safety and throughput.The results offer actionable insights for designing attack-detection and mitigation strategies and lay groundwork for future work in defense, including model-free attack synthesis via reinforcement learning.

Abstract

While automated vehicles (AVs) are expected to revolutionize future transportation systems, emerging AV technologies open a door for malicious actors to compromise intelligent vehicles. As the first generation of AVs, adaptive cruise control (ACC) vehicles are vulnerable to cyberattacks. While recent effort has been made to understanding the impact of attacks on transportation systems, little work has been done to systematically model and characterize the malicious nature of candidate attacks. In this study, we develop a general framework for modeling and synthesizing two types of candidate attacks on ACC vehicles, namely direct attacks on vehicle control commands and false data injection attacks on sensor measurement, with explicit characterization of their adverse effects. Based on linear stability analysis of car-following dynamics, we derive a series of analytical conditions characterizing the malicious nature of potential attacks. This ensures a higher degree of realism in modeling attacks with adverse effects, as opposed to simply considering attacks as constants or random variables. Notably, the conditions derived provide an effective method for strategically synthesizing an array of candidate attacks on ACC vehicles. We conduct extensive simulation to examine the impacts of intelligently designed attacks on microscopic car-following dynamics and macroscopic traffic flow. Numerical results illustrate the mechanism of candidate attacks, offering useful insights into understanding the vulnerability of future transportation systems. The methodology developed allows for further study of the widespread impact of strategically designed attacks on traffic cybersecurity, and is expected to inspire the development of efficient attack detection techniques and advanced vehicle controls.

Figures (9)

• Figure 1: A graphic illustration of mixed traffic involving ACC vehicles and HDVs, with ACC vehicles under potential cyberattacks
• Figure 2: Illustration of two-level controllers for an ACC vehicle, where the upper-level controller determines the desired acceleration while the lower-level controller executes the corresponding throttle or brake input rajamani2011vehicle
• Figure 3: Illustration of the function $\tilde{\lambda}_2$ shown in equation \ref{['eq3.7']} with different ranges of $\delta_{i}$, (a) $\delta_{i} \in \left[-0.5, 1.0\right]$ and (b) $\delta_{i} \in \left[-4, 5\right]$, where $k_1 = 0.05$, $k_2 = 0.10$, and $\tau = 2.50$. The vertical dashed line corresponds to $\delta_{i} = \tau k_1$. It is consistent with the analysis that $\tilde{\lambda}_2$ has two zeros highlighted with red markers, with the larger one being infeasible due to contradiction with \ref{['eq3.5']}. Clearly, for the portion of $\tilde{\lambda}_2$ on the left-hand side of the dashed line, i.e., $\delta_{i} < \tau k_1$, $\tilde{\lambda}_2 \geq 0$ when $\delta_{i} \geq \delta_{i}^*$.
• Figure 4: A graphic illustration of $\hat{\lambda}_2$ given by \ref{['eq4.5']} and $\theta$ shown in \ref{['eq4.16']} with $-1 \leq \delta_{1,i}, \delta_{2,i} \leq 1$, where $k_1 = 0.05$, $k_2 = 0.10$, and $\tau = 2.50$.
• Figure 5: A graphic illustration of a ring of $m$ vehicles in mixed traffic with ACC vehicles under potential attack.

Theorems & Definitions (6)

• Remark 2.1
• Remark 3.1
• Remark 3.2
• Remark 3.3
• Remark 4.1
• Remark 5.1