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
[1] Nobuyuki Kuge. A driver behavior recognition methodbased on a driver model framework[C]∥SAE Technical Paper, 2000-01-0349.
[2] Lethaus F. Do eye movements reflect driving manoeuvres[J]. IET Intelligent Transport Systems,2007, 1(3):199-204.
[3] 王玉海.驾驶员意图与行驶环境的统一识别及实时算法[J].机械工程学报,2006,42(4):206-212.
Wang Yu-hai. Integrated inference of driver's intentions and driving environment and real-time algorithm[J].Chinese Journal of Mechanical Engineering, 2006, 42(4):206-212.
[4] 侯海晶. 高速公路驾驶人换道意图识别方法研究[D]. 长春:吉林大学交通学院, 2013.
Hou Hai-jing.Research on lane-changing intention recognition method for freeway driver[D].Changchun: College of Transportation, Jilin University,2013.
[5] 吕岸,胡振程,陈慧.基于高斯混合隐马尔科夫模型的确高速公路超车行为辨识与分析[J].汽车工程,2010,32(7):630-634.
LV An, Hu Zhen-cheng,Chen Hui. Recognition and analysis on highway overtaking behavior based on gaussian mixture-hidden markov model[J]. Automotive engineering,2010,32(7):630-634.
[6] 贾立山.体现驾驶员特性的车道偏离预警系统关键技术研究[D].武汉:华中科技大学机械科学与工程学院,2011.
Jia Li-shan. Researchof key technologies for lane departure warning system considering the driver characteristics[D].Wuhan: School of Mechanical Science & Engineering, Huazhong University of Science and Technology,2011.
[7] Lerner N.Driver strategies for engaging in distracting tasks using in-vehicle technologies[R]. National Highway Traffic Safety,2010.
[8] Kane M J, Conway A R A, Miura T K, et al. Working memory, attention control, and the N-back task: a question of construct validity[J]. Journal of Experimental Psychology: Learning, Memory, and Cognition, 2007, 33(3): 615.
[9] Brendan Morris. Lane change intent prediction for driver assistance: on-road design and evaluation[C]∥2011 IEEE Intelligent Vehicles Symposium (IV) Baden-Baden, Germany,2011:895-901.
[10] Nakayama O, Tohru F, Nakamura T, et al. Development of a steering entropy method for evaluating driver workload[C]∥Proceeding SAE International Congress and Exposition, 1999:1-10.
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