機械学習を用いた自律走行車向けの耐攻撃型協調適応クルーズコントロール
Resilient Cooperative Adaptive Cruise Control for Autonomous Vehicles Using Machine Learning
V2V通信への攻撃を機械学習で検知・緩和し、CACCの安全性を保つ仕組みRACCONを提案・評価した論文。
著者: Srivalli Boddupalli, Akash Someshwar Rao, Sandip Ray
分類: cs.CR, cs.RO, cs.SY, eess.SY
原文アブストラクト
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.