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基于机非冲突近似网格风险评估的自动驾驶左转运动规划模型

王萍 齐旭东 杨静文 韩瑜 李立 汪贵平 姚俊峰 赵祥模

王萍, 齐旭东, 杨静文, 韩瑜, 李立, 汪贵平, 姚俊峰, 赵祥模. 基于机非冲突近似网格风险评估的自动驾驶左转运动规划模型[J]. 交通运输工程学报, 2022, 22(3): 89-103. doi: 10.19818/j.cnki.1671-1637.2022.03.007
引用本文: 王萍, 齐旭东, 杨静文, 韩瑜, 李立, 汪贵平, 姚俊峰, 赵祥模. 基于机非冲突近似网格风险评估的自动驾驶左转运动规划模型[J]. 交通运输工程学报, 2022, 22(3): 89-103. doi: 10.19818/j.cnki.1671-1637.2022.03.007
WANG Ping, QI Xu-dong, YANG Jing-wen, HAN Yu, LI Li, WANG Gui-ping, YAO Jun-feng, ZHAO Xiang-mo. Left-turn motion planning model of autonomous driving based on approximate grid risk assessment of vehicle-non-motor conflict[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 89-103. doi: 10.19818/j.cnki.1671-1637.2022.03.007
Citation: WANG Ping, QI Xu-dong, YANG Jing-wen, HAN Yu, LI Li, WANG Gui-ping, YAO Jun-feng, ZHAO Xiang-mo. Left-turn motion planning model of autonomous driving based on approximate grid risk assessment of vehicle-non-motor conflict[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 89-103. doi: 10.19818/j.cnki.1671-1637.2022.03.007

基于机非冲突近似网格风险评估的自动驾驶左转运动规划模型

doi: 10.19818/j.cnki.1671-1637.2022.03.007
基金项目: 

国家重点研发计划 2018YFB1600600

国家自然科学基金项目 71901040

详细信息
    作者简介:

    王萍(1982-),女,山东泰安人,长安大学教授,工学博士,从事智能控制理论及其应用研究

    通讯作者:

    齐旭东(1998-),男,陕西宝鸡人,长安大学工学博士研究生

  • 中图分类号: U491.2

Left-turn motion planning model of autonomous driving based on approximate grid risk assessment of vehicle-non-motor conflict

Funds: 

National Key Research and Development Program of China 2018YFB1600600

National Natural Science Foundation of China 71901040

More Information
  • 摘要: 为了提高自动驾驶车辆在复杂机非混行交叉口行车安全性、舒适性和效率,提出了一种基于机非冲突近似网格风险评估的自动驾驶左转运动规划模型,并进行模型泛化;设定静态离散序列交叉口网格区域的划分规则,根据多状态通行行为概率转换关系,预测非机动车在细分网格中的运动状态,并动态评估机非冲突区域的风险等级;在此基础上,采用模型预测方法设计自动驾驶车辆的横纵向控制算法,通过自适应调节航向与速度实现跟踪期望轨迹并同步规避网格冲突区域;结合车辆动力学与外部交互环境等约束条件,开发交叉口四相位信号控制交通仿真平台,采用模型在环测试的方式,从效率优度、舒适性优度、实际规划路径与参考路径的偏移量等方面,验证了对左转机非冲突区域运动规划的有效性。研究结果表明:所提出模型能够有效动态提取和预测网格风险信息,确保自动驾驶车辆与驶入交叉口非机动车的安全交互、高效通行与驾驶舒适性,其规划路径的偏移量与同类算法相比最大可降低17.1%,通行效率最大可提高26.6%,舒适性优度最大可提高39.3%,实际路径跟踪表现出高效通过交叉口机非冲突区域和规划路径占用空间低的明显优势。

     

  • 图  1  不同场景下的冲突点划分

    Figure  1.  Division of conflict points in different scenarios

    图  2  交叉口离散化划分

    Figure  2.  Discrete division of intersection

    图  3  自动驾驶车辆控制系统整体框架

    Figure  3.  Overall framework of autonomous vehicle control system

    图  4  非机动车状态转移

    Figure  4.  Non-motor vehicle state transition

    图  5  车辆动力学模型

    Figure  5.  Vehicle dynamics model

    图  6  交叉口机非冲突场景1

    Figure  6.  Scene 1 of vehicle-non-motor conflict at intersection

    图  7  场景1自动驾驶车辆局部路径规划

    Figure  7.  Local path planning of autonomous vehicles in Scene 1

    图  8  场景1自动驾驶车辆与非机动车实际行驶路径

    Figure  8.  Actual driving paths of autonomous and non-motor vehicles in Scene 1

    图  9  场景1控制系统状态变化

    Figure  9.  State change of control system in Scene 1

    图  10  交叉口机非冲突场景2

    Figure  10.  Scene 2 of vehicle-non-motor conflict at intersection

    图  11  场景2自动驾驶车辆局部路径规划

    Figure  11.  Local path planning of autonomous vehicles in Scene 2

    图  12  场景2自动驾驶车辆与非机动车实际行驶路径

    Figure  12.  Actual driving paths of autonomous and non-motor vehicles in Scene 2

    图  13  场景2控制系统状态变化

    Figure  13.  State change of control system in Scene 2

    图  14  仿真结果对比

    Figure  14.  Comparison of simulation results

    表  1  非机动车不同状态下的速度划分

    Table  1.   Speed distribution in different non-motor vehicle states

    状态 平均速度/ (m·s-1) 标准差 最小速度/ (m·s-1) 最大速度/ (m·s-1)
    停止 0.00 0.10 0.0 0.1
    低速行驶 1.74 0.55 0.4 3.4
    高速行驶 5.20 0.57 3.7 6.9
    下载: 导出CSV

    表  2  三种非机动车离散状态下的方向变化概率

    Table  2.   Direction change probabilities in three discrete states of non-motor vehicles

    方向变化 停止 低速行驶 高速行驶
    左转 0.20 0.15 0.05
    直行 0.60 0.70 0.90
    右转 0.20 0.15 0.05
    下载: 导出CSV
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  • 收稿日期:  2021-12-14
  • 刊出日期:  2022-06-25

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