Jinghan Yang, Andew Estornell, Yevgeniy Vorobeychik (Washington University in St. Louis)

A common vision for large-scale autonomous vehicle deployment is in a ride-hailing context. While this promises tremendous societal benefits, large-scale deployment can also exacerbate the impact of potential vulnerabilities of autonomous vehicle technologies. One particularly concerning vulnerability demonstrated in recent security research involves GPS spoofing, whereby a malicious party can introduce significant error into the perceived location of the vehicle. However, such attack focus on a single target vehicle. Our goal is to understand the systemic impact of a limited number of carefully placed spoofing devices on the quality of the ride hailing service that employs a large number of autonomous vehicles. We consider two variants of this problem: 1) a static variant, in which the spoofing device locations and their configuration are fixed, and 2) a dynamic variant, where both the spoofing devices and their configuration can change over time. In addition, we consider two possible attack objectives: 1) to maximize overall travel delay, and 2) to minimize the number of successfully completed requests (dropping off passengers at the wrong destinations). First, we show that the problem is NP-hard even in the static case. Next, we present an integer linear programming approach for solving the static variant of the problem, as well as a novel deep reinforcement learning approach for the dynamic variant. Our experiments on a real traffic network demonstrate that the proposed attacks on autonomous fleets are highly successful, and even a few spoofing devices can significantly degrade the efficacy of an autonomous ride-hailing fleet.

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Zihao Jin (Microsoft Research and Tsinghua University), Shuo Chen (Microsoft Research), Yang Chen (Microsoft Research), Haixin Duan (Tsinghua University and Quancheng Laboratory), Jianjun Chen (Tsinghua University and Zhongguancun Laboratory), Jianping Wu (Tsinghua University)

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