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基于凸近似的避障原理及无人驾驶车辆路径规划模型预测算法

韩月起 张凯 宾洋 秦闯 徐云霄 李小川 和林 葛建勇 王天培 刘宏伟

韩月起, 张凯, 宾洋, 秦闯, 徐云霄, 李小川, 和林, 葛建勇, 王天培, 刘宏伟. 基于凸近似的避障原理及无人驾驶车辆路径规划模型预测算法. 自动化学报, 2020, 46(1): 153-167. doi: 10.16383/j.aas.2018.c170287
引用本文: 韩月起, 张凯, 宾洋, 秦闯, 徐云霄, 李小川, 和林, 葛建勇, 王天培, 刘宏伟. 基于凸近似的避障原理及无人驾驶车辆路径规划模型预测算法. 自动化学报, 2020, 46(1): 153-167. doi: 10.16383/j.aas.2018.c170287
HAN Yue-Qi, ZHANG Kai, BIN Yang, QIN Chuang, XU Yun-Xiao, LI Xiao-Chuan, HE Lin, GE Jian-Yong, WANG Tian-Pei, LIU Hong-wei. Convex Approximation Based Avoidance Theory and Path Planning MPC for Driver-less Vehicles. ACTA AUTOMATICA SINICA, 2020, 46(1): 153-167. doi: 10.16383/j.aas.2018.c170287
Citation: HAN Yue-Qi, ZHANG Kai, BIN Yang, QIN Chuang, XU Yun-Xiao, LI Xiao-Chuan, HE Lin, GE Jian-Yong, WANG Tian-Pei, LIU Hong-wei. Convex Approximation Based Avoidance Theory and Path Planning MPC for Driver-less Vehicles. ACTA AUTOMATICA SINICA, 2020, 46(1): 153-167. doi: 10.16383/j.aas.2018.c170287

基于凸近似的避障原理及无人驾驶车辆路径规划模型预测算法

doi: 10.16383/j.aas.2018.c170287
基金项目: 

国家自然科学基金 51007003

重庆市科学技术委员会重点专项资助项目 cstc2017jcyjBX0029

详细信息
    作者简介:

    韩月起  长城汽车股份有限公司技术中心智能驾驶系统开发部工程师. 2014年获得山东理工大学学士学位.主要研究为自动驾驶路径规划控制算法设计开发. E-mail: cyaqdlxkz@gwm.cn

    张凯  长城汽车股份有限公司技术中心智能驾驶系统开发部副总工程师. 2003年获得沈阳理工大学学士学位.主要研究方向为自动驾驶系统设计开发. E-mail: zhangkai@gwm.cn

    秦闯  长城汽车股份有限公司技术中心智能驾驶系统开发部工程师. 2015年获得华北水利水电大学学士学位.主要研究方向为自动驾驶路径规划算法开发. E-mail: cyaqdlxkz@gwm.cn

    徐云霄  曾是长城汽车股份有限公司技术中心智能驾驶系统开发部工程师. 2014年获得燕山大学硕士学位.主要研究方向为自动驾驶路径规划算法开发. E-mail: xuyunxiao@chehejia.com

    李小川  长城汽车股份有限公司技术中心智能驾驶系统开发部工程师. 2015年获得河北工业大学城市学院学士学位.主要研究方向为自动驾驶运动规划与多传感器数据融合. E-mail: xchuan.l@foxmail.com

    和林  长城汽车股份有限公司技术中心智能驾驶系统开发部主任工程师. 2006年获得吉林大学车辆工程硕士学位.曾于2006至2014年主导博世第九代ESP系统开发工作.主要研究方向为车辆底盘动态控制, 运动规划控制, 自动驾驶系统多传感器融合, 智能决策. E-mail: helin@gwm.cn

    葛建勇  长城汽车股份有限公司技术中心智能驾驶系统开发部主管工程师. 2012年获得山东理工大学车辆工程学士学位.主要研究方向为底盘动力学控制及自动驾驶系统开发. E-mail: gejianyong@gwm.cn

    王天培  长城汽车股份有限公司技术中心智能驾驶系统开发部主管工程师. 2012年获得北京理工大学硕士学位.主要研究方向为自动驾驶及其关键技术, 数据融合, 决策控制. E-mail: wangtianpei@gwm.cn

    刘宏伟  长城汽车股份有限公司技术中心智能驾驶系统开发部工程师. 2013年获得燕山大学硕士学位.主要研究方向为自动驾驶系统嵌入式开发. E-mail: liuhongwei@gwm.cn

    通讯作者:

    宾洋  工学博士, IEEE会员, 教授.主要研究方向为无人驾驶车辆路径规划/多传感器数据融合技术、燃料电池优化控制, 分布式混合动力电驱动系统, 电流/电压可控双向DC/DC变换器等.本文通信作者E-mail: edward.biny@hotmail.com

Convex Approximation Based Avoidance Theory and Path Planning MPC for Driver-less Vehicles

Funds: 

National Natural Science Foundation of China 51007003

Key Funding Projects of the Chongqing Science and Technology Commission cstc2017jcyjBX0029

More Information
    Author Bio:

    HAN Yue-Qi  Engineer in the Department of Intelligent Driving System Design, Research and Development Center of GWM Company. He received his bachelor degree from Shandong University of Technology in 2014. His research interest covers the research and development of self-driving path planning control algorithm

    ZHANG Kai  Deputy Chief Engineer in the Department of Intelligent Driving System Design, Research and Development Center of GWM Company. He received his bachelor degree from Shenyang Ligong University in 2003. His research interest covers the research and development of self-driving system

    QIN Chuang  Engineer in the Department of Intelligent Driving System Design, Research and Development Center of GWM Company. He received his bachelor degree from North China University of Water Resources and Electric Power in 2015. His research interest covers the research and development of self-driving path planning algorithm

    XU Yun-Xiao  Engineer, who previously worked in the Department of Intelligent Driving System Design, Research and Development Center of GWM Company. He received his master degree from Yanshan University in 2014. His research interest covers the research and development of self-driving path planning algorithm

    LI Xiao-Chuan  Engineer in the Department of Intelligent Driving System Design, Research and Development Center of GWM Company. He received his bachelor degree from Hebei University of Technology City College in 2015. His research interest covers self-driving motion planning and multi-sensor data fusion

    HE Lin  Stafi engineer in the Department of Intelligent Driving System Design, Research and Development Center of GWM Company. He received his master degree from Jilin University in 2006. He was ever leading the Gen 9 ESP development from 2006 to 2014 when working at Bosch. His interest covers vehicle dynamic control, motion control, multi-sensor data fusion, intelligent decision of self-driving car

    GE Jian-Yong  Supervisor engineer in the Department of Intelligent Driving System Design, Research and Development Center of GWM Company. He received his bachelor degree from Shandong University of Technology in 2012. His research interest covers the research and development of chassis dynamic control and self-driving system

    WANG Tian-Pei  Supervisor engineer at the Dept of Intelligent Driving System Design, Research and Development Center of GWM Company. He received his master degree from Beijing University of Technology in 2012. His research interest covers self-driving and its key technologies, data fusion, decision-making control

    LIU Hong-Wei  Engineer in the Department of Intelligent Driving System Design, Research and Development Center of GWM Company. He received his master degree from Yanshan University in 2013. His research interest covers the embedded research and development of self-driving system

    Corresponding author: BIN Yang  Ph. D., IEEE member, Professor. His research interest covers the path planning / multisensor data fusion technology of driverless vehicle, optimization control of fuel cell systems, distributed hybrid electric power propelsion system, current/voltage adjustable bi-directional DC/DC converter etc. Corresponding author of this paper
  • 摘要: 提出了一种改进的无人驾驶车辆路径规划方法, 并搭建了相应的软件在环实时仿真系统, 对方法在结构化道路下的复杂动态交通工况进行性能验证.首先, 引入基于凸近似的避障原理, 对障碍物参考点的选取进行优化, 扩大了路径规划的可行域范围.采用改进后的方法, 并结合模型预测控制(Model predictive control, MPC)原理和曲线坐标系统, 综合考虑自车及障碍车的外形、道路几何约束及自车的机械结构约束、路径最短、侧向加速度、道路对中、逐次变道、车距安全度、左变道优先和前轮转角变化率等权重的影响, 实现了车辆在复杂动态交通工况下的路径规划.最后, 以长城H7运动多用途车作为无人驾驶实验及仿真平台, 搭建基于dSPACE多核架构的Carsim + Simulink软件在环实时仿真系统, 对算法进行验证.结果表明, 所提出的方法不仅可获得合理、平滑的行驶路径, 顺利避开运动障碍车的干扰, 而且具有良好的实时性.
    Recommended by Associate Editor LI Li
    1)  本文责任编委  李力
  • 图  1  车辆模型

    Fig.  1  Vehicle model

    图  2  情景1~3下3种方法的对比结果

    Fig.  2  Comparison of three methods under scenarios 1~3

    图  3  不同参考点计算的可行域对比

    Fig.  3  Feasible area comparison calculated by different reference points

    图  4  改进前后两种凸近似避障法的路径规划结果对比

    Fig.  4  Path planning comparison results between un-developed and developed convex approximation avoidance methods

    图  5  基于曲线坐标系统的车道偏移量计算原理

    Fig.  5  Lane off-set calculation theory based on the curvilinear coordination system

    图  6  车道偏移量与车道线权重值的关系

    Fig.  6  The relationship between lane off-set and cost coefficient

    图  7  权重函数松弛前后结果对比

    Fig.  7  Comparison results of the cost functions before/after relaxation

    图  8  SILS系统实物图

    Fig.  8  Hard-ware of SILS system

    图  9  SILS硬件系统架构图

    Fig.  9  Architecture of the SILS hard-ware system

    图  10  基于SILS的ADAS激光雷达感知系统

    Fig.  10  ADAS lidar sensing system based on SILS

    图  11  长城H7实验样车

    Fig.  11  Prototype vehicle of GWM H7

    图  12  基于SILS的H7 Carsim模型与实车实验性能对比

    Fig.  12  Performance comparison between the H7 Carsim model and prototype vehicle based on SILS

    图  13  静态工况下的仿真结果

    Fig.  13  Simulation results under static scenario

    图  14  动态工况下的仿真结果

    Fig.  14  Simulation results under dynamic scenario

    图  15  弯道动态工况下的仿真结果

    Fig.  15  Simulation results under curvature dynamics scenario

    表  1  长城H7车辆参数值

    Table  1  Vehicle parameters of the GWM H7

    参数 数值 单位 参数 数值 单位
    $k_f$ $-$111187 N/rad $I_z$ 3522.1 ${\rm {kg\cdot m}}^{2}$
    $k_r$ $-$90773 N/rad $M_v$ 2211 kg
    $a$ 1.25 m $v_r$ 6 m/s
    $b$ 1.59 m
    下载: 导出CSV

    表  2  MPC控制系统参数

    Table  2  Parameters of MPC system

    参数 数值 参数 数值
    $k_1$ 0.3 $k_6$ 0.2
    $k_2$ 8 $k_7$ 0.7
    $k_3$ 3 $N$ 3
    $k_4$ 2.5 $N_{\mu}$ 1
    $k_5$ 15
    下载: 导出CSV
    符号 说明 单位
    $a/b$ 前/后轴距离质心的距离 m
    ${\pmb d_{\ast}}$ 自车外观几何形状向量 m
    $D_{0}$ 自车停车时自车和障碍车之间的距离 m
    $I_{z}$ 车辆绕$z$轴的转动惯量 kg$\cdot$s$^2$
    $k_{f/r}$ 前/后轮的侧倾刚度 N/rad
    $l$ 车的轴距 m
    $M$ 障碍车的数量
    $M_{v}$ 车辆质量 kg
    $N/N_{u}$ 预测/控制步长
    $o_{L}$ 车辆质心点$p_{0}$投影到中间车道的最短距离 m
    $OXY/o_{V}x_{V}y_{V}/o_{L}x_{L}y_{L}$
    大地/车辆/道路坐标系
    ${\pmb p}$ 可行域中的点 m
    ${\pmb p_{0}}$ 自车质心位置 m
    ${\pmb q_{\ast}}$ 障碍车参考点
    $r$ 自车凸多边形外形端点数
    $R$ 车辆转弯半径 m
    $s_{*}$ 障碍车凸多边形外形端点数
    $S_{L}$ 车辆质心偏移中间道路的位移量 m
    $S_{L_ {\rm max/min}}$ 左/右道路边缘的极限位置偏移量 m
    $t$ 采样时间 s
    $v_{f/r}$ 自车前/后轴速度 m/s
    $v_{l}$ 障碍车速 m/s
    $v_{la/lg}$ 质心的侧/纵向速度 m/s
    $\dot{v_{la}}$ 质心的侧向加速度 m/s$^{2}$
    $v_{rel}$ 自车和障碍车之间的相对速度 m/s
    ${\pmb w_{\ast}}$ 障碍车(物)外观几何形状向量 m
    $W_{c}$ 车宽 m
    $W_{L}$ 实时测得的车道宽 m
    $x/y$ 自车质心在大地坐标系下的横/纵坐标 m
    $\dot{x}/\dot{y}$ 质心沿大地坐标系$X/Y$轴的速度 m/s
    $\delta_{f}$ 前轮转角 rad
    $\mu_{*}$ 缩放系数
    ${\pmb w}$ 障碍车(物)内部点 m
    $\varphi/\dot{\varphi}/\ddot{\varphi}$ 航向角/横摆角速度/角加速度 rad/rad/s/rad/s$^2$
    $\gamma$ 实际车距与安全车距的比值
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-06-02
  • 录用日期:  2017-12-06
  • 刊出日期:  2020-01-21

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