Adaptive Cruise Control under threat: A stochastic active safety analysis of sensing attacks in mixed traffic

Abstract

Mixed traffic environments combining human-driven vehicles (HDVs) and those equipped with Adaptive Cruise Control (ACC) have already become prevalent. This study tackles the critical yet underexplored threat of sensing attacks, such as jamming and spoofing, on ACC systems. By applying stochastically calibrated ACC and HDV car-following models grounded in field data, we constructed an integrated and high-fidelity framework to simulate mixed traffic. This allows us to comprehensively analyze traffic safety risks enabled by surrogate safety measures, under various sensing attack scenarios and considering mechanisms for cyberattack detection and human intervention. Our findings highlight profound vulnerabilities in traffic safety from sensing attacks, with factors including stochastic driving behaviors, ACC penetration rates, and attack effectiveness. Through scenario-based sensitivity analyses, this research underscores the potential risks more realistically by stochastic simulation and also contributes to the design of detection systems to safeguard mixed traffic. Ultimately, this work provides valuable insights into evaluating the robustness of ACC systems against sensing attacks, supporting the ongoing and future development of effective countermeasures.