吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (1): 38-43.doi: 10.13229/j.cnki.jdxbgxb201501006

• 论文 • 上一篇    下一篇

基于高斯混合模型的驾驶员个人特质辨识

吴坚1,姚琳琳1,朱冰1,2,邓伟文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:2013-09-26 出版日期:2015-02-01 发布日期:2015-02-01
  • 通讯作者: 朱冰(1982),男,副教授,博士.研究方向:汽车地面系统分析与控制,工程仿生学.E-mail:zhubing@jlu.edu.cn
  • 作者简介:吴坚(1977),男,副教授,博士.研究方向:汽车地面系统分析与控制.E-mail:wujian@jlu.edu.cn
  • 基金资助:
    国家自然科学基金项目(51105169, 51175215, 51205156);中国博士后科学基金项目(2011M500053, 2012T50292).

Identification of driver individualities using Gaussian mixture model

WU Jian1,YAO Lin-lin1,ZHU Bing1,2,DENG Wei-wen1   

  1. 1.State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China;
    2.Key Laboratory of Bionic Engineering of Ministry of Education, Jilin University, Changchun 130022, China
  • Received:2013-09-26 Online:2015-02-01 Published:2015-02-01

摘要: 为实现不同类型驾驶员个人特质辨识,基于dSPACE实时仿真平台搭建了驾驶员使用模式信息采集系统,对30名被测驾驶员在典型工况下的使用模式进行了信息采集;采用高斯混合模型建立了驾驶员个人特质辨识模型,选取三类典型驾驶员模本对模型进行了参数训练;利用得到的优化参数对测试驾驶员进行了个人特质类型识别测试,并应用试验设计方法对辨识方法进行了优化分析。测试结果表明,提出的基于高斯混合模型的驾驶员个人特质辨识方法能够有效辨识驾驶员类型。

关键词: 车辆工程, 特征辨识, 高斯混合模型, 驾驶员个人特质

Abstract: In order to identify different types of driver individualities, a driver behavior signal acquisition system was developed using the dSPACE real-time simulation platform. The signals of driving behaviors of 30 drivers were collected under the test condition of double lane change. An identification model of driver individualities was proposed using Gaussian mixture model. Three kinds of typical standard drivers were chosen to optimize the model parameters. Identification experiments were carried out with testing drivers using the optimized model, and the Design of Experiment (DOE) method was applied to analyze the identification method. Experiment results show that the proposed identification method based on Gaussian mixture model can effectively identify the drivers' individualities.

Key words: vehicle engineering, characteristic identification, Gaussian mixture model, driver individualities

中图分类号: 

  • U471.3
[1] Farid M N, Kopf M, Bubb H, et al. Methods to develop a driver observation system used in an active safety system[J]. VDI Berichte, 2006,1960:639-650.
[2] Wang M, Rajamani R. Adaptive cruise control system design and its impact on highway traffic flow[C]∥Proc of American Control Conf Anchorage, AK, 2002: 3690-3695.
[3] 李力,王飞跃,郑南宁,等. 驾驶行为智能分析的研究与发展[J]. 自动化学报,2007,33(10):1015-1022.
Li Li, Wang Fei-yue, Zheng Nan-ning, et al. Research and developments of intelligent driving behavior analysis[J]. Acta Utomatica Sinica, 2007, 33(10): 1015-1022.
[4] Nishiwaki Y, Miyajima C, Kitaoka N, et al. Generation of pedal operation patterns of individual drivers in car-following for personalized cruise control[C]∥Proceeding of Intelligent Vehicles Symposium, 2007: 823-827.
[5] Pentland Alex, Liu Andrew. Modeling and prediction of human behavior[J]. Neural Computation,1999, 11(1):229-242.
[6] Macadam Charles C. Understanding and modeling the human driver[J]. Vehicle System Dynamics, 2003, 40(1):101-134.
[7] Pongsathorn Raksincharoensak. Direct yaw moment control system based on driver behaviour recognition[J]. Vehicle System Dynamics, 2008, 46(1): 911- 921.
[8] Amardeep Sathyanarayana,Pinar Boyraz,Zelam Purohit,et al.Driver adaptive and context aware active safety systems using CAN-bus signals[C]∥Proceeding of IEEE Intelligent Vehicle Symposium,2010:21-24.
[9] 陈国辉. 现代信息科技下创造力的个人特质与社会性分析[J]. 电子测试,2013, 12: 255-256.
Cheng Guo-hui. The analysis of personality trail and socialization of creativity in modern information technology[J]. Electronic Test, 2013, 12: 255-256.
[10] 宗长富, 杨肖,王畅,等. 基于多维高斯隐马尔科夫模型的驾驶员转向行为辨识方法[J]. 吉林大学学报: 工学版, 2009, 39(增刊): 28-31.
Zong Chang-fu, Yang Xiao, Wang Chang, et al. Driving intentions identification and behaviors prediction in car lane change[J]. Journal of Jilin University (Engineering and Technology Edition), 2009, 39(Sup.): 28-31.
[11] 胡江碧,曹新涛. 道路交通事故肇事驾驶员特征分析[J]. 中国公路学报,2009, 22(6):107-110.
Hu Jiang-bi, Cao Xin-tao. Analysis of characteristic of driver involved in road traffic accident[J]. China Journal of Highway and Transport, 2009, 22(6):107-110.
[12] New Tin Lay, Wang Ye. Automatic detection of vocal segments in popular songs[C]∥ISMIR, 2004:138-145.
[13] Reynolds D A, Rose R C. Robust text-independent speaker identification using Gaussian mixture speaker models[J]. IEEE Trans Speech and Audio Processing, 1995, 3(1):72-83.
[1] 常成,宋传学,张雅歌,邵玉龙,周放. 双馈电机驱动电动汽车变频器容量最小化[J]. 吉林大学学报(工学版), 2018, 48(6): 1629-1635.
[2] 席利贺,张欣,孙传扬,王泽兴,姜涛. 增程式电动汽车自适应能量管理策略[J]. 吉林大学学报(工学版), 2018, 48(6): 1636-1644.
[3] 何仁,杨柳,胡东海. 冷藏运输车太阳能辅助供电制冷系统设计及分析[J]. 吉林大学学报(工学版), 2018, 48(6): 1645-1652.
[4] 那景新,慕文龙,范以撒,谭伟,杨佳宙. 车身钢-铝粘接接头湿热老化性能[J]. 吉林大学学报(工学版), 2018, 48(6): 1653-1660.
[5] 刘玉梅,刘丽,曹晓宁,熊明烨,庄娇娇. 转向架动态模拟试验台避撞模型的构建[J]. 吉林大学学报(工学版), 2018, 48(6): 1661-1668.
[6] 赵伟强, 高恪, 王文彬. 基于电液耦合转向系统的商用车防失稳控制[J]. 吉林大学学报(工学版), 2018, 48(5): 1305-1312.
[7] 宋大凤, 吴西涛, 曾小华, 杨南南, 李文远. 基于理论油耗模型的轻混重卡全生命周期成本分析[J]. 吉林大学学报(工学版), 2018, 48(5): 1313-1323.
[8] 朱剑峰, 张君媛, 陈潇凯, 洪光辉, 宋正超, 曹杰. 基于座椅拉拽安全性能的车身结构改进设计[J]. 吉林大学学报(工学版), 2018, 48(5): 1324-1330.
[9] 那景新, 浦磊鑫, 范以撒, 沈传亮. 湿热环境对Sikaflex-265铝合金粘接接头失效强度的影响[J]. 吉林大学学报(工学版), 2018, 48(5): 1331-1338.
[10] 王炎, 高青, 王国华, 张天时, 苑盟. 混流集成式电池组热管理温均特性增效仿真[J]. 吉林大学学报(工学版), 2018, 48(5): 1339-1348.
[11] 金立生, 谢宪毅, 高琳琳, 郭柏苍. 基于二次规划的分布式电动汽车稳定性控制[J]. 吉林大学学报(工学版), 2018, 48(5): 1349-1359.
[12] 隗海林, 包翠竹, 李洪雪, 李明达. 基于最小二乘支持向量机的怠速时间预测[J]. 吉林大学学报(工学版), 2018, 48(5): 1360-1365.
[13] 刘兆惠, 王超, 吕文红, 管欣. 基于非线性动力学分析的车辆运行状态参数数据特征辨识[J]. 吉林大学学报(工学版), 2018, 48(5): 1405-1410.
[14] 王德军, 魏薇郦, 鲍亚新. 考虑侧风干扰的电子稳定控制系统执行器故障诊断[J]. 吉林大学学报(工学版), 2018, 48(5): 1548-1555.
[15] 胡满江, 罗禹贡, 陈龙, 李克强. 基于纵向频响特性的整车质量估计[J]. 吉林大学学报(工学版), 2018, 48(4): 977-983.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!