A Robust CACC Scheme Against Cyberattacks Via Multiple Vehicle-to-Vehicle Networks

TL;DR

This paper proposes a robust CACC scheme that enhances cyberattack resilience by using multiple V2V networks, data fusion, and an $H_{ {infty}}$ controller to maintain platoon safety and performance.

Contribution

It introduces a novel multi-channel data fusion approach combined with a robust $H_{ {infty}}$ controller to improve CACC robustness against cyberattacks.

Findings

The proposed scheme effectively isolates compromised channels.

Simulation results show improved platoon safety and stability.

The approach maintains performance despite communication uncertainties.

Abstract

Cooperative Adaptive Cruise Control (CACC) is a vehicular technology that allows groups of vehicles on the highway to form in closely-coupled automated platoons to increase highway capacity and safety, and decrease fuel consumption and CO2 emissions. The underlying mechanism behind CACC is the use of Vehicle-to-Vehicle (V2V) wireless communication networks to transmit acceleration commands to adjacent vehicles in the platoon. However, the use of V2V networks leads to increased vulnerabilities against faults and cyberattacks at the communication channels. Communication networks serve as new access points for malicious agents trying to deteriorate the platooning performance or even cause crashes. Here, we address the problem of increasing robustness of CACC schemes against cyberattacks by the use of multiple V2V networks and a data fusion algorithm. The idea is to transmit acceleration commands multiple times through different communication networks (channels) to create redundancy at the receiver side. We exploit this redundancy to obtain attack-free estimates of acceleration commands. To accomplish this, we propose a data-fusion algorithm that takes data from all channels, returns an estimate of the true acceleration command, and isolates compromised channels. Note, however, that using estimated data for control introduces uncertainty into the loop and thus decreases performance. To minimize performance degradation, we propose a robust $H_{\infty}$ controller that reduces the joint effect of estimation errors and sensor/channel noise in the platooning performance (tracking performance and string stability). We present simulation results to illustrate the performance of our approach.