Resilient Cooperative Adaptive Cruise Control for Autonomous Vehicles Using Machine Learning

Cooperative Adaptive Cruise Control (CACC) is a fundamental connected vehicle application that extends Adaptive Cruise Control by exploiting vehicle-to-vehicle (V2V) communication. CACC is a crucial ingredient for numerous autonomous vehicle functionalities including platooning, distributed route management, etc. Unfortunately, malicious V2V communications can subvert CACC, leading to string instability and road accidents. In this paper, we develop a novel resiliency infrastructure, RACCON, for detecting and mitigating V2V attacks on CACC. RACCON uses machine learning to develop an on-board prediction model that captures anomalous vehicular responses and performs mitigation in real time. RACCON-enabled vehicles can exploit the high efficiency of CACC without compromising safety, even under potentially adversarial scenarios. We present extensive experimental evaluation to demonstrate the efficacy of RACCON.

Automated summary

Cooperative Adaptive Cruise Control (CACC) is a connected vehicle application that extends Adaptive Cruise Control by utilizing vehicle-to-vehicle communication. Malicious communication can pose threats to the safety and stability of CACC. This paper presents RACCON, a novel resiliency infrastructure that employs machine learning to detect and mitigate V2V attacks on CACC, ensuring the safety of vehicles.