汽车工程 ›› 2021, Vol. 43 ›› Issue (4): 553-561.doi: 10.19562/j.chinasae.qcgc.2021.04.013

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考虑驾驶人特性的智能驾驶路径跟踪算法

金立生1,谢宪毅1(),司法2,郭柏苍1,石健3   

  1. 1.燕山大学车辆与能源学院,秦皇岛 066004
    2.吉林大学交通学院,长春 130022
    3.北京理工大学机械与车辆学院,北京 100081
  • 收稿日期:2020-06-29 出版日期:2021-04-25 发布日期:2021-04-23
  • 通讯作者: 谢宪毅 E-mail:xiexianyi123@126.com
  • 基金资助:
    国家自然科学基金(U19A2069);河北省自然科学基金(E2020203092);河北省重点研发计划项目(20310801D)

Intelligent Driving Path Tracking Algorithm Considering Driver Characteristics

Lisheng Jin1,Xianyi Xie1(),Fa Si2,Baicang Guo1,Jian Shi3   

  1. 1.School of Vehicle and Energy,Yanshan University,Qinhuangdao 066004
    2.Transportation College of Jilin University,Changchun 130022
    3.School of Mechanical Engineering,Beijing Institute of Technology,Beijing 100081
  • Received:2020-06-29 Online:2021-04-25 Published:2021-04-23
  • Contact: Xianyi Xie E-mail:xiexianyi123@126.com

摘要:

针对现有智能汽车路径跟踪控制过程中较少考虑驾驶人特性的问题,设计了一种考虑驾驶人特性的智能驾驶路径跟踪算法。采用k均值算法对实车试验获取的相关数据进行聚类分析,根据操纵特征参数的规律性和差异性将驾驶人特性分为正常型、激进型、保守型3类。根据驾驶人特性分类及聚类结果,将不同驾驶人对车辆侧向、纵向行驶状态的不同偏好特性融入至路径跟踪控制策略的设计中。采用模型预测原理设计了智能驾驶路径跟踪控制器,通过数据聚类结果来设计控制器的代价函数与约束条件。仿真试验结果表明,本文所提出的考虑驾驶人特性的路径跟踪控制策略具有较高的轨迹跟踪精度和速度控制精度,且车辆响应变化能够体现出不同的驾驶人特性,路径跟踪速度误差不超过2%,侧向跟踪误差小于0.13 m。

关键词: 智能汽车, 路径跟踪, 驾驶人特性, 模型预测控制

Abstract:

In view of the fact that most of the existing intelligent vehicle path tracking algorithms take little account of the driver characteristics, an intelligent driving path tracking algorithm based on driver characteristic is proposed. Firstly, k?means algorithm is used to cluster and analyze the relevant data obtained from the real vehicle tests.According to the regularity and difference of handling characteristic parameters, the characteristics of drivers are divided into three types: normal type, radical type and conservative type.Then, according to the classification and clustering results of driver characteristics, the different preferences of different types of drivers for vehicle lateral and longitudinal driving state are integrated into the design of trajectory tracking control strategy. Finally, the intelligent driving path tracking controller is designed based on the model predictive control and the cost function and constraints of the controller are designed based on the results of data clustering. The simulation results demonstrate that the vehicle path tracking control strategy proposed in this paper has high tracking accuracy and speed control accuracy. And the vehicle response changes in the tracking process can reflect the characteristics of different drivers. The path tracking speed error is less than 2% and the lateral tracking error is less than 0.13 m.

Key words: intelligent vehicle, path tracking, driver characteristics, model predictive control