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浙江大学学报(工学版)
浙江大学学报(工学版)  2024, Vol. 58 Issue (5): 1001-1008    DOI: 10.3785/j.issn.1008-973X.2024.05.013
交通工程、土木工程     
基于非合作博弈的车道保持共享控制
章军辉1,2,3(),郭晓满2,3,王静贤2,3,付宗杰2,3,刘禹希2,3
1. 常熟理工学院 电气与自动化工程学院，江苏 苏州 215500
2. 无锡物联网创新中心有限公司，江苏 无锡 214029
3. 江苏省物联网创新中心昆山分中心，江苏 苏州 215347
Shared lane-keeping control based on non-cooperative game theory
Junhui ZHANG1,2,3(),Xiaoman GUO2,3,Jingxian WANG2,3,Zongjie FU2,3,Yuxi LIU2,3
1. School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou 215500, China
2. Wuxi Internet of Things Innovation Center Limited Company, Wuxi 214029, China
3. Kunshan Department, Jiangsu Internet of Things Innovation Center, Suzhou 215347, China
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摘要：

为了减少共驾过程中的人机冲突，提出基于非合作博弈的人机共驾控制策略. 基于线性二自由度汽车模型，采用一阶微分方程对车道保持共享控制问题进行数学描述，利用非合作博弈理论(NCG)描述双驾双控系统中的驾驶控制权分配问题，即通过非合作博弈方法来解决多个决策者共同作用于同一个动力学系统的共享控制问题. 设计控制权博弈模型，采用预瞄偏移距离(POD)，对驾驶人与智能系统的置信度矩阵进行更新，能够实现驾驶人与智能系统之间驾驶控制权的平稳交接. 基于模型预测控制(MPC)框架，采用二次型代价函数及线性不等式约束的形式，将车道保持共享控制的前轮转角决策问题转化成带约束的在线二次规划问题. 基于驾驶人在环的CarSim/Simulink集成环境，对该控制策略进行验证. 结果表明，该控制策略能够较好地兼顾横向运动控制精度及驾驶人的控制权裕度.
关键词： 智能车辆;  人机共驾;  模型预测控制;  博弈论;  预瞄偏移距离（POD）    
Abstract:

A driver-automation shared control strategy based on non-cooperative game (NCG) theory was proposed in order to reduce the conflict operations between the driver and intelligent system during the co-driving. The lane-keeping shared control problem was mathematically described by the first-order differential equation based on the linear two degree-of-freedom vehicle model. The NCG theory was employed to resolve the weight allocation problem of the shared control system, where the decision makers would act on the same dynamic system. The driving control authority was designed. Then the smooth transition of driving control authority between the driver and intelligent system was achieved by utilizing the preview offset distance (POD) to update the confidence matrix. The desired front wheel angle of lane-keeping shared control was transformed into an online quadratic programming problem formulated as a quadratic cost function with linear inequality constraints based on the model predictive control (MPC) framework. The shared control strategy was validated on the driver-in-the-loop CarSim/Simulink platform. Results demonstrate that such strategy can well-guarantee lateral tracking accuracy and the priority of the driver’s control authority.
Key words: intelligent vehicle    shared autonomy    model predictive control    game theory    preview offset distance (POD)
收稿日期: 2023-05-05 出版日期: 2024-04-26
CLC:  U 461  
基金资助: 江苏省博士后科研资助计划资助项目（2020Z411）.
作者简介: 章军辉（1985—），男，高级工程师，博士，从事车路协同智能驾驶的研究. orcid.org/0000-0001-5885-4314.E-mail：zjh34@mail.ustc.edu.cn
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引用本文:

章军辉,郭晓满,王静贤,付宗杰,刘禹希. 基于非合作博弈的车道保持共享控制[J]. 浙江大学学报(工学版), 2024, 58(5): 1001-1008.

Junhui ZHANG,Xiaoman GUO,Jingxian WANG,Zongjie FU,Yuxi LIU. Shared lane-keeping control based on non-cooperative game theory. Journal of ZheJiang University (Engineering Science), 2024, 58(5): 1001-1008.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2024.05.013    或    https://www.zjujournals.com/eng/CN/Y2024/V58/I5/1001

图 1  车路参考模型
图 2  非合作人机共驾策略
图 3  控制权博弈模型
参数数值
簧载质量/kg1 650
质心绕z轴的转动惯量/( kg·m2)3 234
前轴与车辆质心之间的距离/m1.4
后轴与车辆质心之间的距离/m1.65
车身宽度/m1.88
前轮的侧偏刚度/( kN·rad?1)80
后轮的侧偏刚度/( kN·rad?1)80
路面附着系数0.8
迎风面积/m22.0
表 1  整车动力学参数
图 4  驾驶人在环试验台架
驾驶状态参数数值
正常驾驶反应时间/s0.5~1.2
正常驾驶制动减速度/(m·s?2)?2~0
正常驾驶侧偏位移/m?0.6~0.6
激进驾驶反应时间/s0.1~0.7
激进驾驶制动减速度/(m·s?2)?3~0
激进驾驶侧偏位移/m?0.4~0.4
疲劳驾驶反应时间/s0.8~2.0
疲劳驾驶制动减速度/(m·s?2)?4~0
疲劳驾驶侧偏位移/m?0.9~0.9
表 2  驾驶状态特性参数
图 5  不同驾驶状态下的横向位移对比
图 6  不同驾驶状态下驾驶人控制权的对比
图 7  不同驾驶状态下方向盘转角的对比
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