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Journal of ZheJiang University (Engineering Science)  2024, Vol. 58 Issue (5): 1001-1008    DOI: 10.3785/j.issn.1008-973X.2024.05.013
    
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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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 wordsintelligent vehicle      shared autonomy      model predictive control      game theory      preview offset distance (POD)     
Received: 05 May 2023      Published: 26 April 2024
CLC:  U 461  
Fund:  江苏省博士后科研资助计划资助项目(2020Z411).
Cite this article:

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.

URL:

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


基于非合作博弈的车道保持共享控制

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) 
Fig.1 Vehicle-road reference model
Fig.2 Non-cooperative driver-automation shared control strategy
Fig.3 Game-theoretical model of control authority
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
Tab.1 Vehicular dynamics parameters
Fig.4 Driver-in-loop experimental platform
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
Tab.2 Parameters of driving state characteristics
Fig.5 Comparison of lateral displacements under different driving states
Fig.6 Comparison of control authority under different driving states
Fig.7 Comparison of steering wheel angles under different driving states
[1]   胡云峰, 曲婷, 刘俊, 等 智能汽车人机协同控制的研究现状与展望[J]. 自动化学报, 2019, 45 (7): 1261- 1280
HU Yunfeng, QU Ting, LIU Jun, et al Human-machine cooperative control of intelligent vehicle: recent developments and future perspectives[J]. Acta Automatica Sinica, 2019, 45 (7): 1261- 1280
[2]   李克强, 戴一凡, 李升波, 等 智能网联汽车(ICV)技术的发展现状及趋势[J]. 汽车安全与节能学报, 2017, 8 (1): 1- 14
LI Keqiang, DAI Yifan, LI Shengbo, et al State-of-the-art and technical trends of intelligent and connected vehicles[J]. Journal of Automotive Safety and Energy, 2017, 8 (1): 1- 14
[3]   MARCANO M, DÍAZ S, PÉREZ J, et al. A review of shared control for automated vehicles: theory and applications [J]. IEEE Transactions on Human-Machine Systems , 2020, 50(6): 475–491.
[4]   WU Y, WEI H, CHEN X, et al Adaptive authority allocation of human-automation shared control for autonomous vehicle[J]. International Journal of Automotive Technology, 2020, 21 (3): 541- 553
doi: 10.1007/s12239-020-0051-6
[5]   秦增科, 郭烈, 马跃, 等 基于人机协同的车道保持辅助系统研究进展[J]. 工程科学学报, 2021, 43 (3): 355- 364
QIN Zengke, GUO Lie, MA Yue, et al Overview of lane-keeping assist system based on human-machine cooperative control[J]. Chinese Journal of Engineering, 2021, 43 (3): 355- 364
[6]   陈无畏, 王其东, 丁雨康, 等 基于预期偏移距离的人机权值分配策略研究[J]. 汽车工程, 2020, 42 (4): 101- 109
CHEN Wuwei, WANG Qidong, DING Yukang, et al Weight allocation strategy between human and machine based on the preview distance to lane center[J]. Automotive Engineering, 2020, 42 (4): 101- 109
[7]   LI W, XIE Z, ZHAO J, et al Human-machine shared steering control for vehicle lane keeping systems via a fuzzy observer-based event-triggered method[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23 (8): 13731- 13744
doi: 10.1109/TITS.2021.3126876
[8]   何仁, 赵晓聪, 杨奕彬, 等 基于驾驶人风险响应机制的人机共驾模型[J]. 吉林大学学报:工学版, 2021, 51 (3): 799- 809
HE Ren, ZHAO Xiaocong, YANG Yibin, et al Man-machine shared driving model using risk-response mechanism of human driver[J]. Journal of Jilin University: Engineering and Technology Edition, 2021, 51 (3): 799- 809
[9]   LI M, CAO H, LI G, et al A two-layer potential-field-driven model predictive shared control towards driver-automation cooperation[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23 (5): 4415- 4431
[10]   郭烈, 葛平淑, 夏文旭, 等 基于人机共驾的车道保持辅助控制系统研究[J]. 中国公路学报, 2019, 32 (12): 46- 57
GUO Lie, GE Pingshu, XIA Wenxu, et al Lane-keeping control systems based on human-machine cooperative driving[J]. China Journal of Highway and Transport, 2019, 32 (12): 46- 57
[11]   严利鑫, 吴超仲, 贺宜, 等 人机共驾智能车驾驶模式决策属性析取研究[J]. 中国公路学报, 2018, 31 (1): 120- 127
YAN Lixin, WU Chaozhong, HE Yi, et al Research on impact factors extraction for driving mode of intelligent vehicle[J]. China Journal of Highway and Transport, 2018, 31 (1): 120- 127
[12]   刘文丽, 郑玲, 杨威, 等. 基于车辆行驶状态的驾驶人能力评价方法[C]//中国汽车工程学会年会论文集. [S. l. ]: 机械工业出版社, 2018: 60-66.
LIU Wenli, ZHENG Ling, YANG Wei, et al. Evaluation method of driver’s capability based on analysis of vehicle states [C]// Proceedings of SAE-China Congress. [S. l. ]: China Machine Press, 2018: 60–66.
[13]   何仁, 赵晓聪, 王建强 人-车-路交互下的驾驶人风险响应度建模[J]. 中国公路学报, 2020, 33 (9): 236- 250
HE Ren, ZHAO Xiaocong, WANG Jianqiang Modeling of driving risk response under human-vehicle-road interaction[J]. China Journal of Highway and Transport, 2020, 33 (9): 236- 250
[14]   YAN Z, YANG K, WANG Z, et al Intention-based lane changing and lane keeping haptic guidance steering system[J]. IEEE Transactions on Intelligent Vehicles, 2021, 6 (4): 622- 633
doi: 10.1109/TIV.2020.3044180
[15]   NGUYEN A, SENTOUH C, POPIEUL J Sensor reduction for driver-automation shared steering control via an adaptive authority allocation strategy[J]. IEEE/ASME Transactions on Mechatronics, 2018, 23 (1): 5- 16
doi: 10.1109/TMECH.2017.2698216
[16]   YANG K, LIU Y, NA X Preview-scheduled steering assistance control for copiloting vehicle: a human-like methodology[J]. Vehicle System Dynamics, 2020, 58 (4): 518- 544
doi: 10.1080/00423114.2019.1590607
[17]   JI A, LEVINSON D A review of game theory models of lane changing[J]. Transportmetrica A: Transport Science, 2020, 16 (3): 1628- 1647
doi: 10.1080/23249935.2020.1770368
[18]   JI X, YANG K, NA X, et al Shared steering torque for lane change assistance: a stochastic game-theoretic approach[J]. IEEE Transactions on Industrial Electronics, 2019, 66 (4): 3093- 3105
doi: 10.1109/TIE.2018.2844784
[19]   章军辉, 李庆, 陈大鹏 实时多目标权重弯道跟随预测控制[J]. 天津大学学报:自然科学与工程技术版, 2020, 53 (8): 861- 871
ZHANG Junhui, LI Qing, CHEN Dapeng Multi-objective real-time weighted model predictive control for car-following[J]. Journal of Tianjin University: Science and Technology, 2020, 53 (8): 861- 871
[20]   NA X, COLE D J Linear quadratic game and non-cooperative predictive methods for potential application to modelling driver–AFS interactive steering control[J]. Vehicle System Dynamics, 2013, 51 (2): 165- 198
doi: 10.1080/00423114.2012.715653
[21]   刘瑞, 朱西产, 刘霖, 等 基于非合作模型预测控制的人机共驾策略[J]. 同济大学学报:自然科学版, 2019, 47 (7): 1037- 1045
LIU Rui, ZHU Xichan, LIU Lin, et al Cooperative driving strategy based on non-cooperative model predictive control[J]. Journal of Tongji University: Natural Science, 2019, 47 (7): 1037- 1045
[22]   章军辉, 李庆, 陈大鹏 基于BP神经网络的纵向避撞安全辅助算法[J]. 西安交通大学学报, 2017, 51 (7): 140- 147
ZHANG Junhui, LI Qing, CHEN Dapeng Safety assistance algorithm for longitudinal collision avoidance based on BP neural network[J]. Journal of Xi'an Jiaotong University, 2017, 51 (7): 140- 147
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