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.

中文翻译：

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一种强大的 CACC 方案，可抵御通过多个车对车网络进行的网络攻击

协同自适应巡航控制 (CACC) 是一种车辆技术，它允许高速公路上的车辆组形成紧密耦合的自动化队列，以提高高速公路通行能力和安全性，并降低油耗和二氧化碳排放量。CACC 背后的底层机制是使用车对车 (V2V) 无线通信网络向排中的相邻车辆传输加速命令。但是，V2V 网络的使用会导致通信渠道中针对故障和网络攻击的脆弱性增加。通信网络充当恶意代理的新接入点，试图降低队列性能甚至导致崩溃。在这里，我们通过使用多个 V2V 网络和数据融合算法来解决提高 CACC 方案抵抗网络攻击的鲁棒性的问题。这个想法是通过不同的通信网络（通道）多次传输加速命令，以在接收端创建冗余。我们利用这种冗余来获得加速命令的无攻击估计。为了实现这一点，我们提出了一种数据融合算法，该算法从所有通道获取数据，返回对真实加速度命令的估计，并隔离受损通道。但是，请注意，使用估计数据进行控制会给循环带来不确定性，从而降低性能。为了最大限度地减少性能下降，我们提出了一个鲁棒的 $H_{\infty}$ 控制器，它可以减少估计误差和传感器/通道噪声对编队性能（跟踪性能和串稳定性）的联合影响。我们提供了模拟结果来说明我们方法的性能。