The cooperative motion of autonomous vehicles (AVs), which communicate through wireless local area networks (WLANs), has been implemented using various technologies, e.g., the Internet of things (IoT). However, there are several insecure disturbances that occur during the communication of intelligent entities. These disturbances can compromise the cooperative control of AVs. In this paper, we propose a cooperative protocol for the group motion in IoT that provides anti-disturbance protection for AVs. More specifically, we construct group motion models of AVs (GMM-AVs) with mixed disturbances. The disturbances are derived from matched and mismatched disturbances, where the former (the latter) denotes that the disturbances and the control inputs exist in the same (different) tunnels. We design disturbances sensors to detect the loss of GMM-AVs and propose anti-disturbance measures to compensate for the loss. Subsequently, we establish a distributed cooperative control protocol (DCCP-CP) that provides countermeasure protection by analyzing the group cooperation of AVs. We also achieve distributed consensus of AVs with multiple disturbances by studying the dynamic characterization of GMM-AVs. DCCP-CP may achieve cooperative group motion for AVs under the scenario of various disturbances. Finally, the simulations results verify the validity of GMM-AVs and DCCP-CP.

已经通过各种技术（例如，物联网（IoT））实现了通过无线局域网（WLAN）进行通信的自动驾驶车辆（AV）的协同运动。但是，在智能实体的通信过程中会发生几种不安全的干扰。这些干扰可能会损害AV的协同控制。在本文中，我们提出了一种用于物联网中群体运动的协作协议，该协议为视音频提供了抗干扰保护。更具体地说，我们构建具有混合干扰的AV（GMM-AV）的群体运动模型。干扰来自匹配和不匹配的干扰，前者（后者）表示干扰和控制输入存在于相同（不同）的隧道中。我们设计了干扰传感器来检测GMM-AV的损失，并提出了抗干扰措施来补偿损失。随后，我们建立了一个分布式协作控制协议（DCCP-CP），该协议通过分析AV的组协作来提供对策保护。通过研究GMM-AV的动态特性，我们还获得了具有多种干扰的AV的分布式共识。在各种干扰的情况下，DCCP-CP可以实现AV的协作组运动。最后，仿真结果验证了GMM-AV和DCCP-CP的有效性。通过研究GMM-AV的动态特性，我们还获得了具有多种干扰的AV的分布式共识。在各种干扰的情况下，DCCP-CP可以实现AV的协作组运动。最后，仿真结果验证了GMM-AV和DCCP-CP的有效性。通过研究GMM-AV的动态特性，我们还获得了具有多种干扰的AV的分布式共识。在各种干扰的情况下，DCCP-CP可以实现AV的协作组运动。最后，仿真结果验证了GMM-AV和DCCP-CP的有效性。