吉林大学学报(工学版) ›› 2025, Vol. 55 ›› Issue (11): 3593-3603.doi: 10.13229/j.cnki.jdxbgxb.20240109

• 交通运输工程·土木工程 • 上一篇    

面向人机共驾的车道保持预测控制

章军辉1,2,3,4(),刘禹希2,4,郭晓满2,4,刘俊泽2,4,丁羽璇2,4   

  1. 1.苏州工学院 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.江苏省工业机器人复杂工艺智慧控制工程研究中心，江苏 苏州 215500
    4.江苏省物联网创新中心昆山分中心，江苏 苏州 215347
  • 收稿日期:2024-01-29 出版日期:2025-11-01 发布日期:2026-02-03
  • 作者简介:章军辉（1985-），男，高级工程师，博士.研究方向：车路协同，人机共驾技术. E-mail：zjh34@mail.ustc.edu.cn
  • 基金资助:
    江苏省博士后科研计划项目(2020Z411)

Driver-automation cooperation oriented lane-keeping control employing model predictive control approach

Jun-hui ZHANG1,2,3,4(),Yu-xi LIU2,4,Xiao-man GUO2,4,Jun-ze LIU2,4,Yu-xuan DING2,4   

  1. 1.School of Electrical Engineering and Automation，Suzhou University of Technology，Suzhou 215500，China
    2.Wuxi Internet of Things Innovation Center Co. ，Ltd. ，Wuxi 214029，China
    3.Jiangsu Engineering Research Center of Industrial Robot Complex Process Intelligent Control，Suzhou 215500，China
    4.Kunshan Department，Jiangsu Internet of Things Innovation Center，Suzhou 215347，China
  • Received:2024-01-29 Online:2025-11-01 Published:2026-02-03

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摘要：

为了更好地描述共驾型车道保持控制系统（LKAS）的结构和行为，让智辅系统能够预判驾驶人的转向控制意图，提出了一种基于闭环人-车-路模型与态势预测因子的车道保持预测控制算法。首先，引入基于视觉预瞄特性与神经肌肉动力学的驾驶人转向模型，利用粒子群优化（PSO）算法对驾驶人转向模型参数进行离线辨识，并将驾驶人纳入控制回路建立了人-车-路模型；其次，基于模型预测控制（MPC）理论构建约束型二次型代价函数，将共驾型LKAS的转向力矩决策问题转化成带约束的在线二次规划（QP）问题；再次，根据预瞄位置处车辆运动状态的发展态势，设计了一种人机控制权决策模型，用以实现人机控制权的平稳动态分配；最后，基于驾驶人在环的CarSim/Simulink集成环境对该共享控制算法进行了验证与探讨，结果表明：本文提出的共享控制算法能够一定程度上减少智辅系统对驾驶人的干预，即智辅系统能够预判驾驶人的转向控制意图，从而加深共驾过程中智辅系统与驾驶人之间的合作程度，但是以牺牲横向运动控制精度为代价。

关键词: 智能汽车, 人机共驾, MPC控制, 控制权决策, 驾驶人转向模型

Abstract:

In order to better describe the structure and behavior of the shared lane keeping assist system （LKAS）， and hence to enable the intelligent system to predict the driver's steering intention during the co-driving， a novel shared steering control algorithm based on situation prediction factor is thus proposed. Firstly， a driver steering model based on visual preview characteristics and neuromuscular dynamics is introduced. The parameters of such model are identified by particle swarm optimization （PSO） algorithm. By the integration of driver steering model and vehicle-road model， the closed-loop driver-vehicle-road model is established. Secondly， by employing model predictive control （MPC） framework， the decision-making of desired steering torque for shared LKAS is transformed into an online quadratic programming （QP） problem formulated as a quadratic objective function with multiple linear inequality constraints. Then， in order to achieve smooth transition of driving control authority， a driver-automation control authority model is designed using comprehensive preview error approach. Finally， the comparative experimental results demonstrate that by introducing the driver steering model， the intervention from intelligent system， negative intervention especially， can be effectively reduced to a certain extent， but the improved driver-automation friendliness is at the cost of sacrificing the accuracy of lateral motion control.

Key words: intelligent vehicle, shared autonomy, MPC, control authority decision, driver steering model

中图分类号: 

  • U461

图1

共驾型LKAS设计框图"

图2

基于单点预瞄的车路参考模型"

图3

远近视角模型"

图4

驾驶人转向模型"

表1

驾驶人转向模型参数"

参数KPKCτleadτlagτpKRKTτN
数值2.128.02.61.00.040.2720.50.1

图5

驾驶人转向力矩与模型输出对比"

图6

车道偏离参考模型"

图7

人机控制权决策模型，其中α=0.27,λ1=0.8,λ2=0.2"

图8

驾驶人在环试验台架与弯道工况"

表2

整车动力学参数"

参 数数值
整车质量m/kg1 650
质心绕z轴的转动惯量Iz /（kg?m2）3 234
车身宽度/m1.88
前轴与车辆质心之间的距离a/m1.4
后轴与车辆质心之间的距离b/m1.65
前轮的侧偏刚度Cf /（kN?rad-1）92
后轮的侧偏刚度Cr /（kN?rad-1）92
轮胎接触地面宽度ηt /m0.13
转向系等效阻尼系数Bs /（N?m?s?rad-1）0.57
转向系等效转动惯量Js /（kg?m2）0.05
转向传动比is16
道路宽度/m3.75
近视点预瞄距离/m5
远视点预瞄距离/m15
纵向速度vx /（km?h-1）30
采样周期/ms100

图9

横向偏差对比"

图10

驾驶人控制权"

图11

方向盘转角对比"

图12

转向力矩对比"

图13

不同数学模型下的共驾效果对比"

图14

不同控制策略下的共驾效果对比"

表3

综合性能对比"

指标项目JTJD,?phyJD,?psyJA
G策略DVR模型1.04119.58180.27108.11
G策略VR模型0.35125.37235.12180.61

图15

不同驾驶人的共驾效果对比"

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