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基于图与势场法的多车道编队控制

高力 陆丽萍 褚端峰 张勇 吴超仲

高力, 陆丽萍, 褚端峰, 张勇, 吴超仲. 基于图与势场法的多车道编队控制. 自动化学报, 2020, 46(1): 117-126. doi: 10.16383/j.aas.c190052
引用本文: 高力, 陆丽萍, 褚端峰, 张勇, 吴超仲. 基于图与势场法的多车道编队控制. 自动化学报, 2020, 46(1): 117-126. doi: 10.16383/j.aas.c190052
GAO Li, LU Li-Ping, CHU Duan-Feng, ZHANG Yong, WU Chao-Zhong. Multi-lane Convoy Control Based on Graph and Potential Field. ACTA AUTOMATICA SINICA, 2020, 46(1): 117-126. doi: 10.16383/j.aas.c190052
Citation: GAO Li, LU Li-Ping, CHU Duan-Feng, ZHANG Yong, WU Chao-Zhong. Multi-lane Convoy Control Based on Graph and Potential Field. ACTA AUTOMATICA SINICA, 2020, 46(1): 117-126. doi: 10.16383/j.aas.c190052

基于图与势场法的多车道编队控制

doi: 10.16383/j.aas.c190052
基金项目: 

国家重点研究发展计划 2018YFB0105004

国家自然科学基金 51675390

国家自然科学基金 U1764262

湖北省技术创新专项重大项目 2016AAA007

清华大学汽车安全与节能国家重点实验室开发基金 KF1807

详细信息
    作者简介:

    高力  武汉理工大学计算机科学与技术学院硕士研究生.主要研究方向为无人驾驶车辆的编队控制研究.E-mail: jxncgaoli@163.com

    陆丽萍   博士, 武汉理工大学计算机科学与技术学院副教授.主要研究方向为车联网, 计算仿真, 嵌入式系统. E-mail: luliping@whut.edu.cn

    张勇   武汉理工大学计算机科学与技术学院硕士研究生.主要研究方向为基于强化学习的无人驾驶.E-mail: zy826723748@163.com

    吴超仲  博士, 武汉理工大学智能交通系统研究中心教授.主要研究方向为交通安全及车路协同.E-mail: wucz@whut.edu.cn

    通讯作者:

    褚端峰   博士, 武汉理工大学智能交通系统研究中心副教授.主要研究方向为智能网联汽车, 智能交通系统.本文通信作者. E-mail: chudf@whut.edu.cn

Multi-lane Convoy Control Based on Graph and Potential Field

Funds: 

National Key Research Program of China 2018YFB0105004

National Natural Science Foundation of China 51675390

National Natural Science Foundation of China U1764262

the Major Project of Technological Innovation in Hubei Province 2016AAA007

the Science Fund of State Key Laboratory for Automotive Safety and Energy Conservation of Tsinghua University KF1807

More Information
    Author Bio:

    GAO Li    Master student at the School of Computer Science and Technology, Wuhan University of Technology. His main research interest is formation control of unmanned vehicles

    LU Li-Ping    Ph.D., associate professor at the College of Computer Science and Technology, Wuhan University of Technology. Her research interest covers vehicle networking, computational simulation, and embedded system

    ZHANG Yong   Master student at the School of Computer Science and Technology, Wuhan University of Technology. His main research interest is automated driving based on reinforcement learning

    WU Chao-Zhong    Ph.D., professor at the Intelligent Transportation Systems Research Center, Wuhan University of Technology. His research interest covers traffic safety and intelligent vehicle infrastructure cooperative systems

    Corresponding author: CHU Duan-Feng   Ph.D., associate professor at the Intelligent Transportation Systems Research Center, Wuhan University of Technology. His research interest covers automated and connected vehicle and intelligent transportation systems. Corresponding author of this paper
  • 摘要: 多车协同驾驶能显著提高交通安全和效率, 是未来5G网联自动驾驶技术的重要应用场景之一.传统上, 多车协同驾驶的主要形式为单一车道上的无人车队列, 其队列稳定性受队列长度、通信距离及延迟的限制.本文提出一种无人车编队方法, 将单车道队列扩展为多车道护航编队.针对不同场景下的需求设计多车道编队调整策略, 结合基于图的分布式控制, 完成任意预定义的编队结构; 同时, 利用势场法对行车环境建立势场模型, 实现无人车的避障轨迹规划, 提高编队的避障能力; 最后, 结合纵横向控制器, 实现无人车多车道护航编队控制.仿真实验表明, 本文提出的无人车多车道护航编队方法, 能适应不同交通场景, 如道路变化、障碍车运动等, 完成自动变换编队结构, 实现安全、高效通行.
    Recommended by Associate Editor GUO Ge
    1)  本文责任编委 郭戈
  • 图  1  道路曲线坐标示意图

    Fig.  1  Sketch of curvilinear coordinates $(s, l)$

    图  2  编队避障策略示意图

    Fig.  2  Convoys obstacle avoidance process

    图  3  两种编队结构示意图

    Fig.  3  Sketch of two formation structures

    图  4  车辆加入编队示意图

    Fig.  4  Vehicle joining process

    图  5  道路边界势场分布图

    Fig.  5  Sketch of road potential field

    图  6  保持车队势场分布图

    Fig.  6  The 3D distribution of keeping formation

    图  7  换道势场分布图

    Fig.  7  The 3D distribution of lane changing

    图  8  编队内车辆势场影响范围示意图

    Fig.  8  The range of convoy vehicle's potential

    图  9  编队内车辆势场分布图

    Fig.  9  The 3D distribution of convoy vehicle's potential

    图  10  环境车势场影响范围示意图

    Fig.  10  The range of environmental vehicle's potential

    图  11  环境车势场分布图

    Fig.  11  The 3D distribution of evironment vehicle's potential

    图  12  各场景示意图

    Fig.  12  Sketch of each scenario

    图  13  场景1中仿真实验结果

    Fig.  13  Scenario 1 simulation result

    图  14  场景2中仿真实验结果

    Fig.  14  Scenario 2 simulation result

    图  15  场景3中编队仿真实验结果

    Fig.  15  Scenario 3 simulation result

    图  16  场景4中编队仿真实验结果

    Fig.  16  Scenario 4 simulation result

    表  1  势场与控制器相关参数

    Table  1  Parameters of the potentials and the controller and potential field

    参数 单位 参数 单位
    $k_{*}$ 0.8 - $L3$ 135 m
    $\lambda_{*}$ $2.1/K$ - $L4$ 0.8 m
    ${S1}$ 5 m $L$ 3 m
    ${S2}$ 2 m $l_{1}$ 3 -
    ${S3}$ 0.8 m $l_{2}$ 10 -
    ${L1}$ 5 m $\lambda_{1}$ 0.5 -
    ${L2}$ 15 m $K$ 1.25 -
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-01-22
  • 录用日期:  2019-05-23
  • 刊出日期:  2020-01-21

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