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 ma...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2022-09, Vol.23 (9), p.15655-15672
Main Authors: Boddupalli, Srivalli, Rao, Akash Someshwar, Ray, Sandip
Format: Article
Language:English
Subjects:
Adaptive control
Anomaly detection
Connected and autonomous vehicles
Connected vehicles
Cooperative control
Cruise control
Machine learning
Platooning
Prediction models
Reliability engineering
Resilience
Security
Traffic accidents
V2X communication
Vehicle-to-everything
Vehicular ad hoc networks
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Summary: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.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2022.3144599