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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