在存在恶意车辆的情况下，互联和自动驾驶的安全评估和攻击隔离
Secure Estimation and Attack Isolation for Connected and Automated Driving in the Presence of Malicious Vehicles
摘要：
互联和自动驾驶车辆（CAV）依赖于位置和其他车辆运动学信息的正确性来完成各种驾驶任务，例如车辆跟随、车道变换和碰撞避免。然而，恶意车辆可能有意或无意地向其他车辆发送错误的传感器信息，这可能导致交通不便或人命损失。在这里，我们利用云计算的优势，并通过假设每辆车与其他车辆共享其本地传感器信息来增加CAV对恶意车辆的弹性，以在云端创建信息冗余。我们利用这种冗余，并提出了一种传感器融合算法的云，能够提供一个强大的状态估计的所有车辆在云的条件下，恶意信息的数量是足够小的。使用所提出的估计，我们提供了一个算法隔离恶意车辆。我们用数值例子来说明我们的方法的有效性。
英文摘要：
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 cloud-computing 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 cloud, capable of providing a robust state estimation of all vehicles in the cloud 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
