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基于驾驶员转向模型的共享控制系统

田彦涛 赵彦博 谢波

田彦涛, 赵彦博, 谢波. 基于驾驶员转向模型的共享控制系统. 自动化学报, 2022, 48(7): 1664−1677 doi: 10.16383/j.aas.c190486
引用本文: 田彦涛, 赵彦博, 谢波. 基于驾驶员转向模型的共享控制系统. 自动化学报, 2022, 48(7): 1664−1677 doi: 10.16383/j.aas.c190486
Tian Yan-Tao, Zhao Yan-Bo, Xie Bo. Shared control system based on driver steering model. Acta Automatica Sinica, 2022, 48(7): 1664−1677 doi: 10.16383/j.aas.c190486
Citation: Tian Yan-Tao, Zhao Yan-Bo, Xie Bo. Shared control system based on driver steering model. Acta Automatica Sinica, 2022, 48(7): 1664−1677 doi: 10.16383/j.aas.c190486

基于驾驶员转向模型的共享控制系统

doi: 10.16383/j.aas.c190486
基金项目: 国家自然科学基金(U1664263, U19A2069)资助
详细信息
    作者简介:

    田彦涛:吉林大学通信工程学院教授. 吉林省先进控制与自主系统科技创新中心主任. 1993年获得吉林工业大学博士学位. 主要研究方向为复杂系统建模, 优化与控制, 智能机器人系统控制, 电动汽车主动安全系统与智能辅助驾驶. 本文通信作者.E-mail: tianyt@jlu.edu.cn

    赵彦博:吉林大学通信工程学院硕士研究生. 2015年获得吉林大学学士学位. 主要研究方向为智能汽车的建模和控制.E-mail: zhaoyb9080@126.com

    谢波:吉林大学通信工程学院硕士研究生. 2018年获得吉林大学学士学位. 主要研究方向为智能汽车的建模和人机协同控制.E-mail: 18227746645@163.com

  • 中图分类号: 10.16383/j.aas.c190486

Shared Control System Based on Driver Steering Model

Funds: Supported by National Natural Science Foundation of China (U1664263, U19A2069)
More Information
    Author Bio:

    TIAN Yan-Tao Professor at the College of Communication Engineering, Jilin University, and the Director of the Innovation Center for Advanced Control Autonomous Systems of Jilin Province. He received his Ph.D. degree from Jilin University of Technology in 1993. His research interest covers modeling and optimized control of complex systems, intelligent robot system control, active safety systems of the electric vehicles, and advanced driver assistance systems. Corresponding author of this paper

    ZHAO Yan-Bo Master student at the College of Communication Engineering, Jilin University. He received his bachelor degree from Jilin University in 2015. His research interest covers intelligent vehicle modeling and control

    XIE Bo Master student at the College of Communication Engineering, Jilin University. He received his bachelor degree from Jilin University in 2018. His research interest covers intelligent vehicle modeling and man-machine cooperative control

  • 摘要: 针对车辆驾驶对于共享控制系统实用性的需求, 提出了基于驾驶员转向模型的共享控制系统. 基于驾驶员的视觉预瞄特性与神经肌肉特性建立了驾驶员转向模型, 通过遗传算法辨识模型参数并分析其与车速和道路曲率之间的函数关系; 采用模糊权重分配策略合理分配驾驶权重; 本文利用基于所开发的CarMaker驾驶模拟实验平台, 对系统进行在线测试和验证, 结果表明该系统不仅能够更好地提升车辆的轨迹跟踪精度和安全性, 辅助驾驶员转向, 还能够极大地减轻驾驶员负荷.
  • 图  1  间接共享控制结构图

    Fig.  1  Indirect shared control structure

    图  2  驾驶员转向结构示意图

    Fig.  2  Driver steering structure

    图  3  驾驶员两点预瞄示意图[22]

    Fig.  3  Two-point preview of driver[22]

    图  4  驾驶员转向模型

    Fig.  4  Driver steering model

    图  5  模拟驾驶实验场景

    Fig.  5  Driving simulation experiment scenario

    图  6  模拟驾驶实验场景 (单位: m)

    Fig.  6  Driving simulation experiment scenario (unit: m)

    图  7  驾驶员力矩滤波与截取

    Fig.  7  Filtering and interception of driver torque

    图  8  目标函数值(v = 72 km/h, R = 200 m)

    Fig.  8  Value of objective function (v = 72 km/h, R = 200 m)

    图  9  驾驶员力矩测量值与辨识值对比(v = 72 km/h, R = 200 m)

    Fig.  9  Comparison of measurement and identification value (v = 72 km/h, R = 200 m)

    图  10  驾驶员力矩测量值与模型输出值对比

    Fig.  10  Comparison of measurement and model output value of driver torque

    图  11  驾驶员力矩误差值

    Fig.  11  Error value of driver torque

    图  12  不同车速下模型参数平均值对比(R = 200 m)

    Fig.  12  Comparison of mean values of model parameters at different vehicle speeds (R = 200 m)

    图  13  不同车速下模型参数平均值对比(R = 300 m)

    Fig.  13  Comparison of mean values of model parameters at different vehicle speeds (R = 300 m)

    图  14  转弯半径为200 m时神经肌肉反馈刚度与车速的拟合曲线

    Fig.  14  Fitting curve of $K_{t}$ and v at turning radius of 200 m

    图  15  转弯半径为300 m时神经肌肉反馈刚度与车速的拟合曲线

    Fig.  15  Fitting curve of $K_{t}$ and v at turning radius of 300 m

    图  16  不同车速下驾驶员模型辨识误差指标对比(R = 200 m)

    Fig.  16  Comparison of driver model error index at different vehicle speeds (R = 200 m)

    图  17  不同车速下驾驶员模型辨识误差指标对比(R = 300 m)

    Fig.  17  Comparison of driver model error index at different vehicle speeds (R = 300 m)

    图  18  辨识模型与真实测量转向数据对比

    Fig.  18  Comparison of identification model and measurement value

    图  19  数据点与拟合曲线

    Fig.  19  Fitting curve and data point

    图  20  辨识模型值与真实测量值对比

    Fig.  20  Comparison of identification model value and measurement value

    图  21  已辨识模型的输出值与真实测量值对比

    Fig.  21  Comparison of the output value of the identified model with the measured value

    图  22  不同车速下车辆中心点与道路内边界的距离

    Fig.  22  Distance between vehicle center point and road internal boundary at different vehicle speeds

    图  23  不同车速下共享控制器的输出力矩

    Fig.  23  Output torque of shared controller at different vehicle speeds

    图  24  不同车速下方向盘的输出转向角

    Fig.  24  Output steering angle of steering wheel at different speeds

    图  25  不同车速下车辆的路径跟踪偏差

    Fig.  25  Vehicle path tracking deviation at different speeds

    图  26  人机共驾测试示意图

    Fig.  26  Man-machine co-driving test

    图  27  驾驶员A正常转向下驾驶员转角与权重后的输出转向角对比

    Fig.  27  Contrast of driver's angle with output steering angle after weight under driver A's normal steering

    图  28  驾驶员A正常转向下控制器权重的变化

    Fig.  28  Variation of controller weight under driver A's normal steering

    图  29  驾驶员A正常转向下车辆的路径跟踪偏差

    Fig.  29  Vehicle path tracking deviation under driver A's normal steering

    图  30  驾驶员B过度转向下驾驶员转角与权重后的输出转向角对比

    Fig.  30  Contrast of driver's angle under driver B oversteering with output steering angle after weighting

    图  31  驾驶员B过度转向下控制器权重的变化

    Fig.  31  Variation of controller weight under driver B's oversteering

    图  32  驾驶员B过度转向下车辆的路径跟踪偏差

    Fig.  32  Vehicle path tracking deviation under driver B's oversteering

    图  33  驾驶员C转向不足时驾驶员转角与权重后的输出转向角对比

    Fig.  33  Contrast the driver's angle with weighted output steering angle when C is insufficient turning

    图  34  驾驶员C转向不足时车辆的路径跟踪偏差

    Fig.  34  Vehicle path tracking deviation when driver C steering is insufficient

    图  35  驾驶员C单独驾驶与共享驾驶下转向角对比

    Fig.  35  Contrast of insufficient steering angle between driver C driving alone and shared driving

    图  36  驾驶员C单独驾驶与共享驾驶下转向力矩对比

    Fig.  36  Contrast of steering torque between driver C driving alone and shared driving

    图  37  驾驶员C单独驾驶与共享驾驶下驾驶员各项指标对比

    Fig.  37  Contrast of driver's index between driver C driving alone and shared driving

    图  38  驾驶员C单独驾驶的路径跟踪偏差

    Fig.  38  Vehicle path tracking deviation when driver C driving alone

    图  39  驾驶员C共享驾驶下的路径跟踪偏差

    Fig.  39  Vehicle path tracking deviation under shared driving for driver C

    表  1  遗传算法主要参数取值

    Table  1  Value of main parameters of genetic algorithm

    序号参数取值
    1种群个数1500
    2染色体基因数(待辨识参数个数)8
    3交叉概率0.8
    4变异概率0.1
    5最大迭代次数1000
    6选择操作的算法选取“轮盘赌”选择法
    下载: 导出CSV

    表  2  转弯半径为200 m的驾驶员模型参数

    Table  2  Driver model parameters with turning radius of 200 m

    $v\;({\rm{km/h}})$$K_{p}$$K_{c}$$T_{I}$$T_{L}$$\tau_{p}$$K_{r}$$K_{t}$$T_{N}$
    541st0.1418.671.760.270.620.2811.320.01
    2nd0.1122.491.870.210.680.3011.110.01
    3rd0.1220.571.680.280.580.2012.050.01
    均值0.1220.581.770.250.630.2611.490.01
    631st0.2114.231.680.180.580.2513.470.01
    2nd0.1715.882.070.290.610.3112.990.01
    3rd0.1519.531.730.330.660.2913.380.01
    均值0.1816.551.830.270.620.2813.280.01
    721st0.2515.051.990.200.590.3815.090.01
    2nd0.1716.022.250.210.690.3514.960.01
    3rd0.3312.771.960.260.630.2615.430.01
    均值0.2514.612.070.220.640.3315.160.01
    901st0.2110.522.480.170.910.118.490.01
    2nd0.1110.372.140.981.240.1118.190.01
    3rd0.2410.213.380.781.640.1818.250.01
    均值0.1910.372.670.641.260.1318.310.01
    下载: 导出CSV

    表  3  转弯半径为300 m的驾驶员模型参数

    Table  3  Driver model parameters with turning radius of 300 m

    $v\;({\rm{km/h}})$$K_{p}$$K_{c}$$T_{I}$$T_{L}$$\tau_{p}$$K_{r}$$K_{t}$$T_{N}$
    721st0.0529.881.560.570.790.2415.280.01
    2nd0.1129.951.110.890.640.1415.610.01
    3rd0.0929.571.380.640.580.1115.080.01
    均值0.0829.801.350.700.670.1615.320.01
    901st0.0923.421.551.130.850.1618.680.01
    2nd0.0623.571.180.970.690.1318.920.01
    3rd0.1324.051.040.780.630.2118.420.01
    均值0.0923.681.260.960.720.1718.670.01
    1081st0.1221.720.970.811.170.3121.160.01
    2nd0.1822.290.920.941.220.4421.200.01
    3rd0.1423.520.820.861.150.6121.060.01
    均值0.1522.510.900.871.180.4521.140.01
    1261st0.0321.860.532.691.190.1324.580.01
    2nd0.0520.260.482.141.110.1825.210.01
    3rd0.0819.170.393.050.890.3325.860.01
    均值0.0720.430.472.631.060.2125.220.01
    下载: 导出CSV

    表  4  驾驶员模型的辨识误差指标(R = 200 m)

    Table  4  Identification error index of driver model (R = 200 m)

    v (km/h)和方差 SSE均方差 MSE均方根 RMSE采样点个数
    541st1.5350.00080.02921800
    2nd1.9390.00110.03281800
    3rd1.8650.00100.03211800
    均值1.7790.00090.03141800
    631st1.8360.00110.03381600
    2nd1.6760.00100.03241600
    3rd1.9780.00120.03521600
    均值1.8300.00110.03381600
    721st1.7840.00130.03561400
    2nd1.2850.00090.03001400
    3rd1.7680.00120.03551400
    均值1.6120.00110.03371400
    901st6.4880.00650.08051000
    2nd5.0640.00510.07121000
    3rd6.7800.00680.08231000
    均值6.1110.00620.07801000
    下载: 导出CSV

    表  5  驾驶员模型的辨识误差指标(R = 300 m)

    Table  5  Identification error index of driver model (R = 300 m)

    v (km/h)和方差 SSE均方差 MSE均方根 RMSE采样点个数
    721st0.9040.00050.02241800
    2nd0.5660.00030.01771800
    3rd0.8020.00040.02111800
    均值0.7570.00040.02041800
    901st2.5440.00160.03991600
    2nd1.2690.00080.02821600
    3rd2.5030.00160.03951600
    均值2.1050.00130.03591600
    1081st4.1940.00290.05471400
    2nd5.3140.00380.06161400
    3rd4.2340.00300.05501400
    均值4.5810.00320.05711400
    1261st29.3400.02450.15641000
    2nd33.1800.02770.16631000
    3rd16.9240.01410.11881000
    均值26.4810.02210.14721000
    下载: 导出CSV

    表  6  不同车速下的驾驶员模型参数辨识结果

    Table  6  Identification of driver model parameters at different vehicle speed

    参数54 (km/h)63 (km/h)72 (km/h)83 (km/h)94 (km/h)
    $K_{p}$0.210.190.140.090.08
    $K_{c}$49.9942.5549.7949.9549.99
    $T_{i}$4.313.485.115.23.72
    $T_{L}$0.320.300.340.220.21
    $\tau_{p}$0.670.670.720.780.68
    $K_{r}$0.320.330.420.520.48
    $K_{t}$11.6613.6815.1216.9518.99
    $T_{N}$0.010.010.010.010.01
    目标函数0.0930.160.431.535.06
    峰值误差0.0370.0490.0820.140.287
    和方差 (SSE)0.3740.320.863.0510.14
    均方差 (MSE)0.00020.00020.00060.00280.01
    均方根 (RMSE)0.01440.01410.02480.0530.1
    平均误差0.0070.0110.0180.030.079
    下载: 导出CSV

    表  7  不同车速下的车辆路径跟踪偏差指标对比

    Table  7  Comparison of route tracking deviation index at different vehicle speeds

    类型63 km/h72 km/h83 km/h94 km/h108 km/h120 km/h
    峰值误差0.270.210.140.230.711.36
    和方差 (SSE)26.613.62.6117.12142.47481.2
    均方差 (MSE)0.0150.0090.0020.0150.1410.523
    均方根 (RMSE)0.1250.0950.0450.1220.3750.723
    下载: 导出CSV

    表  8  不同驾驶模式下车辆的路径跟踪偏差指标对比

    Table  8  Comparison of vehicle path tracking deviation index under different driving mode

    驾驶行为峰值误差和方差 (SSE)均方差 (MSE)均方根 (RMSE)
    A 正常转向0.44628.890.0120.11
    B 过度转向0.20710.760.00450.067
    C 转向不足0.4359.70.0250.158
    C 单独驾驶0.78277.570.11570.34
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-06-26
  • 录用日期:  2020-02-23
  • 网络出版日期:  2022-06-13
  • 刊出日期:  2022-07-01

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