论文标题
智能城市中的混合交通流量的分布式动态排控制和交叉交叉优化 - 第二部分。稳定性，优化和性能分析
Distributed Dynamic Platoons Control and Junction Crossing Optimization for Mixed Traffic Flow in Smart Cities- Part II. Stability, Optimization, and Performance Analysis
论文作者
论文摘要
在第二部分中，我们提出了一个完全分布的非线性可变时间前进策略，以确保随后的安全巡航和交界处的交叉点，在此过程中，对多个邻居刺激的合作感知以及对自动连接的自动车辆（CAVS）的合作跟踪会导致对领导者的cav开发，从而导致了导致异质流量流量流量流量。一旦形成了第一部分中特征的ADSCA确定的排列的适当长度，我们就提出了一个合作观察者设计，以估算由未知交通信号灯影响的领导者CAV的加速调整。 We shall show that the distributed and resilient nonlinear platoons control and junction crossing problem will be solved by a robust cooperative trajectory tracking optimization algorithm to ensure the fast formation and split of the platoons and safe junction cruising within the finite time horizons, taking into account the social driving behaviors(SDBs) of the surrounding vehicles(SVs), the dynamics of the follower CAVs, and an upcoming traffic signal在最小化整体排油耗的同时安排时间安排。提出了绩效分析和案例研究，以说明提出的多种排动态管理的拟议方法的有效性，这还表明，骑士与人类驱动的车辆（HDV）之间的合作可以进一步平滑驾驶轨迹，减少燃油消耗，并增强混合交通流的安全性。
In part II, we present a fully distributed nonlinear variable time headway space strategy to ensure the subsequent safe cruising and junction crossing, where the cooperative perception of multiple neighbors stimuli and the cooperative tracking of the follower connected automated vehicles(CAVs) to the leader CAV are developed, which will result in a heterogeneous traffic flow dynamic. Once the proper length of the platoon determined by the ADSCAS characterized in part I is formed, we propose a cooperative observer design to estimate the leader CAV's acceleration adjustment which is affected by the unknown traffic lights. We shall show that the distributed and resilient nonlinear platoons control and junction crossing problem will be solved by a robust cooperative trajectory tracking optimization algorithm to ensure the fast formation and split of the platoons and safe junction cruising within the finite time horizons, taking into account the social driving behaviors(SDBs) of the surrounding vehicles(SVs), the dynamics of the follower CAVs, and an upcoming traffic signal schedule while minimizing the overall platoons fuel consumption. Performance analysis and case studies are presented to illustrate the effectiveness of the proposed approaches for multiple platoon dynamic management, which also show that the cooperation between CAVs and human-driven vehicles(HDVs) can further smooth out the driving trajectory, reduce the fuel consumption, and enhance the safety of the mixed traffic flow
