基于多集群系统的车辆协同换道控制

谢光强; 赵俊伟; 李杨; 许浩然

doi:10.12052/gdutxb.210050

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基于多集群系统的车辆协同换道控制

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- 基于多集群系统的车辆协同换道控制
- Cooperative Lane-changing Based on Multi-cluster System
- 广东工业大学学报 2021年38卷第5期页码：1-9
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
  
  谢光强(1979–)，男，教授，博士，主要研究方向为多智能体、智能控制、差分隐私保护
  李杨(1980–)，女，教授，博士，主要研究方向为多智能体、差分隐私保护，E-mail：liyang@gdut.gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(61876043，61472089)
- DOI：10.12052/gdutxb.210050
  中图分类号： TP391
- 纸质出版：2021
- 稿件说明：
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谢光强, 赵俊伟, 李杨, 许浩然. 基于多集群系统的车辆协同换道控制[J]. 广东工业大学学报, 2021, 38(5): 1-9.

Xie Guang-qiang, Zhao Jun-wei, Li Yang, et al. Cooperative Lane-changing Based on Multi-cluster System[J]. 2021, 38(5): 1-9.
谢光强, 赵俊伟, 李杨, 许浩然. 基于多集群系统的车辆协同换道控制[J]. 广东工业大学学报, 2021, 38(5): 1-9. DOI： 10.12052/gdutxb.210050.

Xie Guang-qiang, Zhao Jun-wei, Li Yang, et al. Cooperative Lane-changing Based on Multi-cluster System[J]. 2021, 38(5): 1-9. DOI： 10.12052/gdutxb.210050.

摘要

针对智能联网环境下的多车协同换道问题

设计一个基于多集群系统的车辆协同控制框架。给出了虚拟领导者的选取条件

智能联网车辆通过分布式集群划分算法选取邻居车辆、领导者、虚拟领导者作为控制协议的状态演化。在此基础上

提出适用于集群空间分配的间距控制算法和基于领导者跟随者的集群控制协议

使换道车辆扩大前后车辆纵向距离以达到安全换道间距。理论分析采取多微分方程求解的方式

证明所提控制协议能保证集群内的局部一致性和集群间的群一致性。仿真实例表明

所提算法与控制协议实现多集群速度收敛一致

车辆间保持期望安全间距以稀疏队形稳定行驶

多辆车辆安全准确换道到目标车道。

Abstract

Aiming at the problem of multi vehicle cooperative control in an environment with connected and automated vehicles

a cooperative lane changing framework is proposed based on multi cluster system. The selection conditions of virtual leader are given. Connected and Automated Vehicles (CAVs) select neighbors

leader and virtual leader as the state evolution of control protocol through distributed clustering algorithm. On this basis

a space control algorithm for cluster space allocation and a leader-follower control protocol based on cluster system are designed to make the target vehicles extend the longitudinal distance between adjacent vehicles to achieve safe lane changing distance. Theoretical analysis verifies that the proposed control protocol can guarantee the local consistency within the cluster and the group consistency among clusters by solving multiple differential equations. The simulation results show that the proposed algorithm and control protocol can make the multi clusters converge uniformly and Cavs maintain an expected safe distance to drive stably in a sparse formation. As a result

CAVs change safely and accurately to the target lane.

关键词

Keywords

references

JIN W L. A kinematic wave theory of lanechanging vehicular traffic [J]. Transportation Research Part B, 2005, 44(8): 1001-1021.

YANG Q, KOUTSOPOULOS H N. A microscopic traffic simulator for evaluation of dynamic traffic management systems [J]. Transportation Research Part C: Emerging Technologies, 1996, 4(3): 113-129.

JULA H, KOSMATOPOULOS E B, IOANNOU P A. Collision avoidance analysis for lane changing and merging [J]. IEEE Transactions on Vehicular Technology, 2000, 49(6): 2295-2308.

CHOWDHURY D, WOLF D E, SCHRECKENBERG M. Particle hopping models for two-lane traffic with two kinds of vehicles: effects of lane-changing rules [J]. Physica A: Statistical Mechanics and Its Applications, 1997, 235(3-4): 417-439.

WANG Y M, ZHOU L S, LYU Y B. Lane changing rules based on cellular automaton traffic flow model [J]. China Journal of Highway and Transport, 2008(1): 93-97.

LI X, MA W Z, ZHAO Z F, et al . Improved STCA lane changing model for two-lane road based on driving guidance under CVIS [J ] . Journal of Southeast University (Natural Science Edition), 2020, 50(6): 147-155.

ALONSO J, MILANÉS V, PÉREZ J, et al . Autonomous vehicle control systems for safe crossroads [J ] . Transportation Research Part C: Emerging Technologies, 2011, 19(6): 1095-1110.

KATRAKAZAS C, QUDDUS M, CHEN W H, et al . Real-time motion planning methods for autonomous on-road driving: state-of-the-art and future research directions [J ] . Transportation Research Part C: Emerging Technologies, 2015, 60: 416-442.

YOU F, ZHANG R, LIE G, et al . Trajectory planning and tracking control for autonomous lane change maneuver based on the cooperative vehicle infrastructure system [J ] . Expert Systems with Applications, 2015, 42(14): 5932-5946.

YANG G, ZHANG D H, LI K Q, et al . Cooperative same-direction automated lane-changing based on vehicle-to-vehicle communication [J ] . Journal of Highway and Transportation Research and Development, 2017, 34(01): 120-129.

LI B, ZHANG Y, FENG Y, et al . Balancing computation speed and quality: a decentralized motion planning method for cooperative lane changes of connected and automated vehicles [J ] . IEEE Transactions on Intelligent Vehicles, 2018, 3(3): 340-350.

XU M, LUO Y, YANG G, et al. Dynamic cooperative automated lane-change maneuver based on minimum safety spacing model[C]//2019 IEEE Intelligent Transportation Systems Conference (ITSC). Auckland: IEEE, 2019: 1537-1544.

韩静文. 智能网联条件下的多车协同换道研究[D]. 镇江: 江苏大学, 2020.

BAI Y, ZHANG Y, HU J. A motion planner enabling cooperative lane changing: reducing congestion under partially connected and automated environment [J]. Journal of Intelligent Transportation Systems, 2020: 1-13.

XIE G Q, HUANG C, LI Y, et al . Hybrid control for enhancing consensus of multiagent systems [J ] . Application Research of Computers, 2020, 37(8): 2315-2319.

XIE G Q, DU Y F, CHEN J Y, et al . Consensus protocol based on switching topology and event triggering mechanism [J ] . Application Research of Computers, 2021, 38(3): 770-776.

ZHANG Z H, PENG S G, et al . Leader-following consensus of second-order multi-agent systems with switching topology [J ] . Journal of Guangdong University of Technology, 2018, 35(2): 75-80.

LI S E, ZHENG Y, LI K, et al . Dynamical modeling and distributed control of connected and automated vehicles: Challenges and opportunities [J ] . IEEE Intelligent Transportation Systems Magazine, 2017, 9(3): 46-58.

TALEBPOUR A, MAHMASSANI H S. Influence of connected and autonomous vehicles on traffic flow stability and throughput [J]. Transportation Research Part C: Emerging Technologies, 2016, 71: 143-163.

JIA D, LU K, WANG J, et al . A survey on platoon-based vehicular cyber-physical systems [J ] . IEEE Communications Surveys Tutorials, 2015, 18(1): 263-284.

DING J, LI L, PENG H, et al . A rule-based cooperative merging strategy for connected and automated vehicles [J ] . IEEE Transactions on Intelligent Transportation Systems, 2019, 21(8): 3436-3446.

SANTINI S, SALVI A, VALENTE A S, et al . Platooning maneuvers in vehicular networks: a distributed and consensus-based approach [J ] . IEEE Transactions on Intelligent Vehicles, 2018, 4(1): 59-72.

LI Y, LI K, CAI L, et al. Feedback-based platoon control for connected autonomous vehicles under different communication network topologies[C]//2016 35th Chinese Control Conference (CCC). Chengdu: IEEE, 2016: 8806-8811.

LUO H F, PENG S G. Distributed formation control for multi-agent systems with multiple time-varying delays [J]. Journal of Guangdong University of technology, 2017, 34(4): 89-96.

LI Y, TANG C, LI K, et al . Consensus-based cooperative control for multi-platoon under the connected vehicles environment [J ] . IEEE Trans on Intelligent Transportation Systems, 2018, 20(6): 2220-2229.

李姗. 车辆队列间协同换道控制策略[D]. 北京: 清华大学, 2018.

DESIRAJU D, CHANTEM T, HEASLIP K. Minimizing the disruption of traffic flow of automated vehicles during lane changes [J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 16(3): 1249-1258.

MEISSNER E, CHANTEM T, HEASLIP K. Optimizing departures of automated vehicles from highways while maintaining mainline capacity [J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(12): 3498-3511.

ZHENG Y, RAN B, QU X, et al . Cooperative lane changing strategies to improve traffic operation and safety nearby freeway off-ramps in a connected and automated vehicles environment [J ] . IEEE Transactions on Intelligent Transportation Systems, 2019, PP(99): 1-10.

XU T, ZHANG Z, WU X, et al . Recognition of lane-changing behavior with machine learning methods at freeway off-ramps [J ] . Physica A: Statistical Mechanics and its Applica tions, 2020: 125691.

HU X, SUN J. Trajectory optimization of connected and autonomous vehicles at a multilane freeway merging area [J]. Transportation Research Part C: Emerging Technologies, 2019, 101: 111-125.

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