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Cooperative Adaptive Cruise Control (CACC) is a fundamental connected vehicleapplication that extends Adaptive Cruise Control by exploitingvehicle-to-vehicle (V2V) communication. CACC is a crucial ingredient fornumerous autonomous vehicle functionalities including platooning, distributedroute management, etc. Unfortunately, malicious V2V communications can subvertCACC, leading to string instability and road accidents. In this paper, wedevelop a novel resiliency infrastructure, RACCON, for detecting and mitigatingV2V attacks on CACC. RACCON uses machine learning to develop an on-boardprediction model that captures anomalous vehicular responses and performsmitigation in real time. RACCON-enabled vehicles can exploit the highefficiency of CACC without compromising safety, even under potentiallyadversarial scenarios. We present extensive experimental evaluation todemonstrate the efficacy of RACCON.

Resilient Cooperative Adaptive Cruise Control for Autonomous Vehicles Using Machine Learning