In face of an increasing number of automotive cyber-physical threat scenarios, the issue of adversarial destabilization of the lateral motion of target vehicles through direct attacks on their steering systems has been extensively studied. A more subtle question is whether a cyberattacker can destabilize the target vehicle lateral motion through improper engagement of the vehicle brakes and/or anti-lock braking systems (ABS). Motivated by such a question, this paper investigates the impact of cyber-physical attacks that exploit the braking/ABS systems to adversely affect the lateral motion stability of the targeted vehicles. Using a hybrid physical/dynamic tire-road friction model, it is shown that if a braking system/ABS attacker manages to continuously vary the longitudinal slips of the wheels, they can violate the necessary conditions for asymptotic stability of the underlying linear time-varying (LTV) dynamics of the lateral motion. Furthermore, the minimal perturbations of the wheel longitudinal slips that result in lateral motion instability under fixed slip values are derived. Finally, a real-time algorithm for monitoring the lateral motion dynamics of vehicles against braking/ABS cyber-physical attacks is devised. This algorithm, which can be efficiently computed using the modest computational resources of automotive embedded processors, can be utilized along with other intrusion detection techniques to infer whether a vehicle braking system/ABS is experiencing a cyber-physical attack. Numerical simulations in the presence of realistic CAN bus delays, destabilizing slip value perturbations obtained from solving quadratic programs on an embedded ARM Cortex-M3 emulator, and side-wind gusts demonstrate the effectiveness of the proposed methodology.
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Targeted Attack on Deep RL-based Autonomous Driving with Learned Visual Patterns
Recent studies demonstrated the vulnerability of control policies learned through deep reinforcement learning against adversarial attacks, raising concerns about the application of such models to risk-sensitive tasks such as autonomous driving. Threat models for these demonstrations are limited to (1) targeted attacks through real-time manipulation of the agent's observation, and (2) untargeted attacks through manipulation of the physical environment. The former assumes full access to the agent's states/observations at all times, while the latter has no control over attack outcomes. This paper investigates the feasibility of targeted attacks through visually learned patterns placed on physical objects in the environment, a threat model that combines the practicality and effectiveness of the existing ones. Through analysis, we demonstrate that a pre-trained policy can be hijacked within a time window, e.g., performing an unintended self-parking, when an adversarial object is present. To enable the attack, we adopt an assumption that the dynamics of both the environment and the agent can be learned by the attacker. Lastly, we empirically show the effectiveness of the proposed attack on different driving scenarios, perform a location robustness test, and study the tradeoff between the attack strength and its effectiveness Code is available at https://github.com/ASU-APG/ Targeted-Physical-Adversarial-Attacks-on-AD
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- NSF-PAR ID:
- 10392509
- Date Published:
- Journal Name:
- 2022 International Conference on Robotics and Automation (ICRA)
- Page Range / eLocation ID:
- 10571 to 10577
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICRA46639.2022.9811574
