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车辆安全跟驰模式预测的形式化建模方法

刘秉政 高松 曹凯 王鹏伟 徐艺

刘秉政, 高松, 曹凯, 王鹏伟, 徐艺. 车辆安全跟驰模式预测的形式化建模方法. 自动化学报, 2021, 47(10): 2364−2375 doi: 10.16383/j.aas.c190563
引用本文: 刘秉政, 高松, 曹凯, 王鹏伟, 徐艺. 车辆安全跟驰模式预测的形式化建模方法. 自动化学报, 2021, 47(10): 2364−2375 doi: 10.16383/j.aas.c190563
Liu Bing-Zheng, Gao Song, Cao Kai, Wang Peng-Wei, Xu Yi. Formal modeling method for prediction of safe vehicle following mode. Acta Automatica Sinica, 2021, 47(10): 2364−2375 doi: 10.16383/j.aas.c190563
Citation: Liu Bing-Zheng, Gao Song, Cao Kai, Wang Peng-Wei, Xu Yi. Formal modeling method for prediction of safe vehicle following mode. Acta Automatica Sinica, 2021, 47(10): 2364−2375 doi: 10.16383/j.aas.c190563

车辆安全跟驰模式预测的形式化建模方法

doi: 10.16383/j.aas.c190563
基金项目: 1. 国家自然科学基金(61573009), 山东省自然科学基金(ZR2018LF009, ZR2018PEE016)资助
详细信息
    作者简介:

    刘秉政:山东理工大学交通与车辆工程学院博士后. 2017年获得大连理工大学博士学位. 主要研究方向为智能车辆行为预测与决策. E-mail: lbzheng528@126.com

    高松:山东理工大学交通与车辆工程学院院长, 教授. 主要研究方向为电动车辆能源动力系统匹配理论与控制技术, 智能车辆与智能交通系统. 本文通信作者. E-mail: gs6510@163.com

    曹凯:山东理工大学交通与车辆工程学院教授. 2005年获得日本茨城大学博士学位. 主要研究方向为车路协同控制与建模, 车辆自主行为决策建模, 基于数据链的交通空-地协同信息融合. E-mail: caokailiu@sdut.edu.cn

    王鹏伟:山东理工大学交通与车辆工程学院博士研究生. 2015年获得江西农业大学机械设计与理论专业硕士学位. 主要研究方向为智能车辆动态决策与规划, 智能车辆动力学与控制. E-mail: wpwk16@163.com

    徐艺:山东理工大学交通与车辆工程学院讲师. 2016年获得吉林大学载运工具运用工程专业博士学位. 主要研究方向为智能车环境感知, 动态决策与规划. E-mail: xuyisdut@163.com

Formal Modeling Method for Prediction of Safe Vehicle Following Mode

Funds: Supported by National Natural Science Foundation of China (61573009), Natural Science Foundation of Shandong province, China (ZR2018LF009, ZR2018PEE016)
More Information
    Author Bio:

    LIU Bing-Zheng Postdoctor at the School of Transportation and Vehicle Engineering, Shandong University of Technology. He received his Ph.D. degree from Dalian University of Technology in 2017. His research interest covers intelligent vehicle behavior decisionand planning

    GAO Song Dean and professor at the School of Transportation and Vehicle Engineering, Shandong University of Technology. His research interests include energy system matching theory and control technology of electric vehicle, intelligent vehicle and intelligent transportation system. Corresponding author of this paper

    CAO Kai Professor at the School of Transportation and Vehicle Engineering, Shandong University of Technology. He received his Ph.D. degree from the Ibaraki University of Japan in 2005. His research interest covers vehicle-road collaborative control and modeling, decision modeling of vehicle autonomous behavior and information fusion based on air-ground collaboration data-link

    WANG Peng-Wei Ph.D. candidate at the School of Transportation and Vehicle Engineering, Shandong University of Technology. He received his master degree from the Jiangxi Agriculture University in 2015. His research interest covers intelligent vehicle dynamic decision and planning, intelligent vehicle dynamics and control

    XU Yi Lecturer with the School of Transportation and Vehicle Engineering, Shandong University of Technology. He received his Ph.D. degree in vehicle operation engineering from Jilin University in 2016. His research interest covers intelligent vehicle environment perception, dynamic decision and planning

  • 摘要: 由于传统车辆跟驰建模预测方法无法遍历车辆所有可能的系统输入与运行状态的不确定性, 因而不足以从理论上保证对周边车辆安全跟驰行为预测的完整性与可信性. 为此提出车辆安全跟驰模式预测的形式化建模方法. 该方法利用随机可达集的遍历表现特征实现对周边车辆行为预测的不确定性表述, 并通过马尔科夫链逼近可达集的方式表达系统行为状态变化的随机性, 从而完成对周边车辆跟驰行为状态变化的精确概率预估. 为了表达跟驰情形中车辆之间的行为关联影响以及提高在线计算效率, 离线构建了关联车辆在状态及控制输入之间的安全关联矩阵, 描述周边车辆的安全跟驰控制输入选择规律, 并综合相关车辆的当前状态信息, 达到对周边车辆安全跟驰行为的在线分析与预估. 数值验证不仅表明提出的建模方法完备地表述了周边车辆所有的安全跟驰行为及过程, 显著提高了预测的精确度, 也论证了该方法对车辆跟驰控制策略建模分析与安全验证的有效性.
  • 图  1  建模框架

    Fig.  1  Method framework

    图  2  仿真流程

    Fig.  2  Simulation flowchart

    图  3  不同时间区段的车辆踪迹分布

    Fig.  3  Trace distribution of vehicles for different time intervals

    图  4  不同时间区段的跟驰车辆控制输入直方图

    Fig.  4  Control input histograms of following vehicles for different time intervals

    图  5  不同时间区段的跟驰车辆速度直方图

    Fig.  5  Velocity histograms of following vehicles for different time intervals

    图  6  不同时间区段的车辆踪迹分布

    Fig.  6  Trace distribution of vehicles for different time intervals

    图  7  跟驰情形中的碰撞概率

    Fig.  7  Collision probability in vehicle following

    图  8  不同时间区段的车辆踪迹分布

    Fig.  8  Trace distribution of vehicles for different time intervals

    图  9  跟驰情形中的碰撞概率

    Fig.  9  Collision probability in vehicle following

    表  1  离线运算中主要参数

    Table  1  Main parameters used in offline operation

    参数赋值
    $S / \mathrm{m}$ $[0,200]$
    $V / \mathrm{(m/s)}$$[0,20]$
    $U$$[-1,1]$
    $n$$40$
    $m$$10$
    $g$$6$
    $\varpi$$10$
    $\varepsilon $$0.000 1$
    下载: 导出CSV

    表  2  驾驶行为及车辆特性

    Table  2  Driving behavior and vehicle characteristics

    参数赋值
    $\gamma$$0.2$
    $\pmb \mu$$[0.01\;0.04\;0.1\;0.4\;0.4\;0.05]$
    $\pmb q_{(i,j)}(0)$$[0\;0\;0\;1\;0\;0]$
    $\tau / \mathrm{s}$$0.5$
    $\sigma$$[1\;4\;8]$
    $a^\mathrm{max} / \mathrm{(m/s^2)}$$7$
    $v^* \mathrm{(m/s)}$$7.3$
    下载: 导出CSV

    表  3  初始属性-1: 均匀分布集合

    Table  3  Initial state-1: Set with uniform distribution

    参数赋值
    $S^\mathrm{A}(0) / \mathrm{m}$$[100,106]$
    $V^\mathrm{A}(0) / \mathrm{(m/s)}$$[2,4]$
    $S^\mathrm{B}(0) / \mathrm{m}$$[50,62]$
    $V^\mathrm{B}(0) / \mathrm{(m/s)}$$[8,10]$
    $S^\mathrm{C}(0) / \mathrm{m}$$[5,17]$
    $V^\mathrm{C}(0) / \mathrm{(m/s)}$$[12,14]$
    下载: 导出CSV

    表  4  初始属性-2: 均匀分布集合

    Table  4  Initial state-2: Set with uniform distribution

    参数赋值
    $S^\mathrm{A}(0)\; / \mathrm{m}$$[62, 74]$
    $V^\mathrm{A}(0)\; / \mathrm{(m/s)}$$[8, 10]]$
    $S^\mathrm{B}(0)\; / \mathrm{m}$$[25, 37]$
    $V^\mathrm{B}(0)\; / \mathrm{(m/s)}$$[6, 8]$
    $S^\mathrm{C}(0)\; / \mathrm{m}$$[5, 17]$
    $V^\mathrm{C}(0)\; / \mathrm{(m/s)}$$[2, 4]$
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-08-01
  • 录用日期:  2019-12-02
  • 网络出版日期:  2019-12-26
  • 刊出日期:  2021-10-20

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