Connected and Automated Vehicles (CAVs) rely on the correctness of position and other vehicle kinematics information to fulfill various driving tasks such as vehicle following, lane change, and collision avoidance. However, a malicious vehicle may send false sensor information to the other vehicles intentionally or unintentionally, which may cause traffic inconvenience or loss of human lives. Here, we take the advantage of vehicular cloud and increase the resilience of CAVs to malicious vehicles by assuming each vehicle shares its local sensor information with other vehicles to create information redundancy on the cloud side. We exploit this redundancy and propose a sensor fusion algorithm for the vehicular cloud, capable of providing robust state estimation of all vehicles under the condition that the number of malicious information is sufficiently small. Using the proposed estimator, we provide an algorithm for isolating malicious vehicles. We use numerical examples to illustrate the effectiveness of our methods.

中文翻译：

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存在恶意车辆的情况下互联和自动驾驶的安全估计和攻击隔离


联网自动驾驶车辆 (CAV) 依靠位置和其他车辆运动学信息的正确性来完成车辆跟随、变道和避免碰撞等各种驾驶任务。然而，恶意车辆可能会有意或无意地向其他车辆发送虚假的传感器信息，这可能会导致交通不便或人员伤亡。在这里，我们利用车辆云的优​​势，假设每辆车与其他车辆共享其本地传感器信息，在云端创建信息冗余，从而提高 CAV 对恶意车辆的抵御能力。我们利用这种冗余，提出了一种用于车辆云的传感器融合算法，能够在恶意信息数量足够小的情况下提供所有车辆的鲁棒状态估计。使用所提出的估计器，我们提供了一种用于隔离恶意车辆的算法。我们使用数值示例来说明我们方法的有效性。