论文标题
分布式合作控制和连接的自动车辆排的优化，以防止社会驱动力的切入行为
Distributed Cooperative Control and Optimization of Connected Automated Vehicles Platoon Against Cut-in Behaviors of Social Drivers
论文作者
论文摘要
互联的自动车辆（CAVS）为改善交通吞吐量并减少能源消耗带来了新的机会。但是，周围车辆（SVS）的不确定的车道变化行为（LCB）是一个无法控制的因素，这显着威胁了一组排骑士的驾驶安全性和一致的运动。如何确保安全，高效和燃料经济排的控制构成了复杂交通环境中研究人员面临的关键挑战。这项研究提出了一个动态的排管理和合作驾驶框架，用于混合交通流量，包括多个骑士和可能的人类驱动车辆（HDV）作为未信号的道路上的SVS。在拟议的框架中，排的领导者骑士通过对HDVS的最佳轨迹估算进行了最佳的轨迹估计，而分布式的观察者和跟踪控制器是由追随者骑士正确实现的，从而为追随者骑士提供了高级自动驾驶决策。具体而言，提议的框架包括三个阶段。在观察阶段，Leader Cav将通过细胞 - 车辆到X（C-V2X）基础设施收集所有SV的巡航信息，而自动决策制定驾驶援助系统（ANDDSS）则是为了确定排列的驾驶状态。当HDV接近该排的通信范围时，在预测阶段，将估算HDVS的轨迹，并通过使用C-V2X基础结构来分别激活领导者CAV的参考轨迹计划和追随者骑士的合作控制器设计。提出了模拟案例，以说明所提出的方法的有效性。
Connected automated vehicles (CAVs) have brought new opportunities to improve traffic throughput and reduce energy consumption. However, the uncertain lane-change behaviors (LCBs) of surrounding vehicles (SVs) as an uncontrollable factor significantly threaten the driving safety and the consistent movement of a group of platoon CAVs. How to ensure safe, efficient, and fuel economic platoon control poses a key challenge faced by researchers in complex traffic environments. This study proposes a dynamic platoon management and cooperative driving framework for a mixed traffic flow consisting of multiple CAVs and possible human-driven vehicles (HDVs) as the SVs on unsignalized roads. In the proposed framework, the leader CAV of the platoon provides a high-level automatic driving decision to the follower CAVs by developing an optimal trajectory estimation of the HDVs while distributed observers and tracking controllers are properly implemented by the follower CAVs. Specifically, the proposed framework consists of three stages. At the observation stage, the cruising information of all the SVs will be collected by the leader CAV through the Cellular-Vehicle-to-X (C-V2X) infrastructure, while an automatic decision-making driving assistance system (ADMDSS) is constructed to determine the driving states of the platoon. When the HDVs approach the communication range of the platoon, in the prediction stage, the trajectories of the HDVs as the target vehicles will be estimated and the reference trajectory planning for the leader CAV and the cooperative controller design for the follower CAVs will be respectively activated by using C-V2X infrastructure. Simulation cases are presented to illustrate the effectiveness of the proposed approaches.
