Position verification systems for an automated highway system.

Biswas, Bidisha; Gerdes, Ryan; Heaslip, Kevin · 2015 · ROSA P / Mountain Plains Consortium

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Summary

This research addresses the cybersecurity vulnerabilities of Automated Highway Systems (AHS), specifically focusing on the risks posed by False Data Injection (FDI) attacks on vehicle platoons. While automated vehicles offer benefits such as improved safety, fuel efficiency, and reduced congestion through adaptive cruise control (ACC) and inter-vehicular communication, their reliance on computer-controlled longitudinal motion makes them susceptible to cyber-physical attacks. The study is motivated by the lack of existing research examining FDI attacks in the context of autonomous vehicle platoons, despite significant attention given to similar threats in smart grids and wireless sensor networks. The primary objective is to analyze how an attacker can exploit system configurations to introduce arbitrary errors into state variables, thereby gaining control over the platoon and compromising string stability. The methodology involves a comprehensive survey of nine longitudinal vehicle motion models, followed by the development of linearized and nonlinear test-beds to simulate an $n$-vehicle platoon. The researchers modeled the platoon using absolute and error dynamics frameworks to evaluate system responses under various attack scenarios. Three primary attack vectors were analyzed: (1) the injection of constant errors into transmitted sensor data, (2) the manipulation of victim vehicles’ acceleration by an attacker with access to their states, and (3) the introduction of time delays in information transmission. Additionally, the study examined the impact of these attacks on systems employing Proportional-Integral-Derivative (PID) control and those experiencing inherent oscillations. Simulations were conducted to observe spacing errors, velocity deviations, and string stability metrics under these compromised conditions. The findings demonstrate that FDI attacks significantly destabilize vehicle platoons. In linear models, injecting constant errors or manipulating acceleration caused follower vehicles to deviate from desired spacing and velocity, leading to string instability where spacing errors propagated and amplified through the platoon. In nonlinear models incorporating time delays and rate limits, the attacks resulted in vehicles failing to reach desired velocities and exhibiting increasing spacing errors, further confirming instability. The study also found that when oscillations were present in the system, FDI attacks exacerbated the risk of collisions, particularly when the oscillation frequency matched the system’s natural frequency. Even with PID control, certain attack scenarios led to early collisions and a complete loss of string stability, indicating that standard control mechanisms are insufficient to mitigate these specific cyber threats. The significance of this work lies in its identification of critical security gaps in automated highway systems. By demonstrating that FDI attacks can arbitrarily manipulate vehicle dynamics and compromise the safety of platoons, the research highlights the urgent need for robust detection and mitigation strategies in vehicular cyber-physical systems. The study concludes that current longitudinal control designs are vulnerable to deception attacks that exploit sensor data and communication channels. These findings imply that future AHS development must integrate cybersecurity measures alongside control algorithms to ensure that inter-vehicular communication remains trustworthy and that platoons can maintain string stability even under adversarial conditions.

Key finding

False data injection attacks on automated vehicle platoons can destroy string stability, causing inter-vehicular spacing errors to grow unbounded and leading to collisions.

Methodology

modeling

Provenance

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