吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (3): 709-716.doi: 10.13229/j.cnki.jdxbgxb201703003

• 论文 • 上一篇    下一篇

驾驶员无意识车道偏离识别方法

高振海, LeDinhDat, 胡宏宇, 孙翊腾   

  1. 吉林大学 汽车仿真与控制国家重点实验室,长春 130022
  • 出版日期:2017-05-20 发布日期:2017-05-20
  • 通讯作者: 胡宏宇(1982-),男,副教授.研究方向:智能汽车,驾驶行为分析.E-mail:huhongyu@jlu.edu.cn
  • 作者简介:高振海(1973-),男,教授,博士生导师.研究方向:汽车驾驶辅助系统,无人驾驶与驾驶行为分析.E-mail:gaozh@jlu.edu.cn
  • 基金资助:

    国家自然科学基金项目(U1564214,51675224)

Recognition method of driver's unintentional lane departure

GAO Zhen-hai, LE DinhDat, HU Hong-yu, SUN Yi-teng   

  1. State Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun 130022,China
  • Online:2017-05-20 Published:2017-05-20

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摘要：

为了改进车道偏离预警系统的工作效能,本文提出了考虑人-车-路特性的无意识车道偏离识别方法。首先,明确了无意识车道偏离识别的具体含义,将其划分为疲劳车道偏离和次任务车道偏离;其次,利用受试者工作特性曲线(ROC)确定无意识车道偏离的识别时间窗口,保证了无意识偏离样本筛选的有效性;再次,以12名驾驶人为试验对象,采集并对比分析了驾驶员操纵特性、车辆运动状态和车辆与车道线相对运动状态等相关参数,并分别选取作为疲劳车道偏离和次任务车道偏离识别基本特征;最后,采用高斯混合隐马尔科夫模型(GM-HMM)构建无意识车道偏离识别模型。实验结果表明,本文方法具有较好的识别效果。

关键词: 车辆工程, 先进驾驶辅助系统, 车道偏离预警, 无意识车道偏离, 高斯混合隐马尔科夫模型

Abstract:

In order to improve the performance of lane departure warning system, an unintentional lane departure analysis method is proposed. This method combines the driver's operation characteristics, vehicle's motion characteristics and the relationship between the vehicle and the lane. First, the unintentional lane departure is classified into two parts: lane departure by fatigue and lane departure by secondary task. Then, experiments of unintentional lane departure are carried out through the co-simulation platform based on CarSim and LabWIEW. Twelve drivers of different genders, proficiencies and driving behaviors are selected to participate the experiments. The unintentional lane departure parameters are collected and analyzed, including the driver's operation behavior, the motion characteristics of the vehicles and the relative motion position between the vehicle and the lane. Finally, an unintentional lane departure recognition model is constructed based on Gaussian Mixture-Hidden Markov Model (GM-HMM). The recognition results show good performance of the proposed model in online and offline tests.

Key words: vehicle engineering, advanced driver assistant system(ADAS), lane departure warning(LDW), unintentional lane departure, Gaussian mixture-hidden Markov model (GM-HMM)

中图分类号: 

  • U461.91
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