汽车工程

汽车工程 ›› 2019, Vol. 41 ›› Issue (2): 213-218.doi: 10.19562/j.chinasae.qcgc.2019.02.014

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基于随机森林的驾驶人驾驶习性辨识策略*

朱冰1,2, 李伟男1, 汪震1, 赵健1, 何睿1, 韩嘉懿1   

  1. 1.吉林大学,汽车仿真与控制国家重点实验室,长春 130022;
    2.吉林大学,工程仿生教育部重点实验室,长春 130022
  • 收稿日期:2018-06-29 出版日期:2019-02-25 发布日期:2019-02-25
  • 通讯作者: 何睿,副教授,工学博士,E-mail:herui@jlu.edu.cn
  • 基金资助:
    国家自然科学基金(51775235,51475206)、国家重点研发计划项目(2016YFB0100904)和吉林省自然科学基金(20170101138JC)资助。

Identification Strategy of Driving Style Based on Random Forest

Zhu Bing1,2, Li Weinan1, Wang Zhen1, Zhao Jian1, He Rui1, Han Jiayi1   

  1. 1.Jilin University, State Key Laboratory of Automotive Simulation and Control, Changchun 130022;
    2.Jilin University, Key Laboratory of Bionic Engineering of Ministry of Education, Changchun 130022
  • Received:2018-06-29 Online:2019-02-25 Published:2019-02-25

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摘要： 深入理解驾驶人驾驶习性及其表征方法,对于实现在汽车自动驾驶、辅助驾驶等不同控制系统下的人机和谐交互具有重要意义。为此,本文中提出了一种基于随机森林模型的驾驶人驾驶习性辨识策略。搭建了驾驶人驾驶数据实车采集系统,在典型跟车驾驶工况下对驾驶人驾驶习性数据进行了实时采集;根据层次聚类理论,对驾驶人驾驶习性进行了标定;在此基础上,引入随机森林模型建立了驾驶人驾驶习性辨识策略,并进行了重要性分析、模型训练和测试分析。测试结果表明,本文提出的基于随机森林模型的驾驶人驾驶习性辨识策略能有效辨识驾驶人驾驶习性,模型整体精准度可达97.1%。

关键词: 车辆工程, 驾驶习性辨识, 随机森林, 层次聚类, 跟车工况

Abstract: Understanding and identification of driver's driving style are of great significance to the human-machine harmonious interaction under different control systems such as automatic driving and assistant driving. A driving style identification strategy based on random forest model is proposed in this paper. Firstly, the driver's driving data acquisition system is set up. Based on that, the driving data of several drivers are collected in real time under typical car-following scenarios. According to hierarchical clustering theory, the driving style are “labeled”. On this basis, a random forest model is introduced to establish driving style identification strategy, and importance analysis, model training and identification test are carried out. The test results show that the driving style identification strategy based on the random forest model can effectively identify driver's driving style and the overall accuracy of the model can reach 97.1%

Key words: vehicle engineering, driving style identification, random forest, hierarchical clustering, car-following condition