吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (5): 1233-1239.doi: 10.7964/jdxbgxb201405002

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车辆虚拟跟随避撞中驾驶人制动时刻模型

高振海1, 2, 3, 吴涛1, 4, 赵会2, 3   

  1. 1.吉林大学 汽车仿真与控制国家重点实验室,长春 130022;
    2.汽车噪声振动和安全技术国家重点实验室,重庆 401120;
    3.长安股份有限公司 汽车工程研究院,重庆 401120;
    4.陕西重型汽车有限公司 汽车工程研究院,西安 710200
  • 收稿日期:2013-09-10 出版日期:2014-09-01 发布日期:2014-09-01
  • 通讯作者: 吴涛(1982),男,博士研究生.研究方向:汽车智能辅助驾驶系统.E-mail:wutao10@mails.jlu.edu.cn
  • 作者简介:高振海(1973), 男, 教授, 博士生导师.研究方向:汽车智能辅助驾驶系统.E-mail:gaozh.jlu@gmail.com
  • 基金资助:
    “973”计划前期研究专项(2012CB723802); 高等学校博士学科点专项科研基金项目(20120061110028); 吉林省科技引导计划项目(20130413058GH).

Model of driver's braking moment in virtual car following collision avoidance scenes

GAO Zhen-hai1,2,3,WU Tao1,4,ZHAO Hui2,3   

  1. 1.State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China;
    2.State Key Laboratory of Vehicle NVH and Safety Technology, Chang′an Automobile Holding, Chongqing 401120, China;
    3.Changan Automobile Holding Ltd. Automotive Engineering Institute, Chongqing 401120, China;
    4.Shanxi Heavy Duty Automobile Co. Ltd. Automotive Engineering Institute,Xi'
    an 710200,China
  • Received:2013-09-10 Online:2014-09-01 Published:2014-09-01

摘要: 为探索跟随避撞中驾驶人制动时刻的影响因素,应用汽车驾驶模拟器对多名被试驾驶人进行虚拟交通情景下制动行为测试。利用采集的驾驶人在不同交通情景中制动时刻数据,分析了驾驶人制动时刻与其影响因素之间关系,提出了基于驾驶人采取全制动时刻的危险判断指标,分别建立了多元线性回归、BP 神经网络驾驶人制动时刻模型,并对两者的预测性能进行对比。结果表明:驾驶人的年龄、性别和两车运动状态是影响驾驶人制动时刻的重要因素;建立的BP 神经网络模型预测精度高于回归模型,可用于揭示驾驶人在跟随避撞中危险判断机理,为驾驶辅助系统开发提供了一个具有体现人的个体差异能力的车辆安全行驶状态判断指标,将对改善驾驶辅助系统性能具有重要意义。

关键词: 车辆工程, 驾驶人行为, 跟随避撞, 制动时刻, 预测模型, 虚拟场景

Abstract: To explore the impact factors of driver's braking moment in car following under emergency conditions, the virtual driving behavior tests of several drivers were conducted on a driving simulators. Based on the driver's braking onset data under different scenarios, the impact factors of driver's braking moment were analyzed, and a new threat assessment measure, time to maximum deceleration level braking, was proposed. This measure can directly quantify the danger or threat level of the current dynamic situation. Then two braking moment forecasting models were established by applying linear regression and BP neural network respectively. Results show that the driver's age and gender, the motion state between two cars are the important impact factors. Comparisons show that the BP neural network forecasting model predicts more accurately, that is in agreement with the human natural judgment on the urgency and severity of threat.

Key words: vehicle engineering, driver behavior, following collision avoidance, braking moment, forecast model, virtual scene

中图分类号: 

  • U471.3
[1] 公安部. 2011年全国道路交通事故统计分析[R]. 公安部通报, 2011, 1(1): 1-59.
[2] Reason J. Driving errors, driving violations and accident involvement[J]. Ergonomics, 1995, 38(5): 1036-1048.
[3] Kondoh T, Yamamura T, Kitazaki S, et al. Identification of visual cues and quantification of drivers' perception of proximity risk to the lead vehicle in car-following situations[J]. Journal of Mechanical Systems for Transportation and Logistics, 2008, 1(2): 170-180.
[4] Adrian C, Sven G, Alcherio M. A new collision warning system for lead vehicles in read-end collisions[C]∥2012 Intelligent Vehicles Symposium, Alcal de Henares,Spain,2012: 674-679.
[5] Nakaoka M, Raksincharoensak P, Nagai M. Study on forward collision warning system adapted to driver characteristics and road environment[C]∥IEEE International Conference on Control, Automation and Systems, United States,2008: 2890-2895.
[6] Brunson S J, Kyle E M, Phamdo N C, et al. Alert algorithm development program: NHTSA rear-end collision alert algorithm[R]. NHTSA Technical Report,2002.
[7] 王双超. 前方防碰撞预警系统决策算法开发与实验验证[D]. 长春:吉林大学汽车工程学院, 2012.Wang Shuang-chao. Decision algorithm development and test validation for forward collision warning system[D]. Changchun: College of Automotive Engineering,Jilin University, 2012.
[8] Wada T, Tsuru N, Isaji K, et al. Characterization of expert drivers' last-second braking and its application to a collision avoidance system[J].IEEE Transactions on Intelligent Transportation Systems, 2010,11(2): 413-422.
[9] Kodaka K, Otabe M, Urai Y, et al. Rear-end collision velocity reduction system[C]∥SAE Paper,2003-01-0503.
[10] 李世武, 田晶晶, 沙学锋,等. 基于模糊综合评价和BP神经网络的车辆危险状态辨识[J]. 吉林大学学报:工学版, 2011, 41(6): 1609-1613. Li Shi-wu, Tian Jing-jing, Sha Xue-feng, et al. Identification of vehicle risk status based on fuzzy comprehensive evaluation and BP neural network[J]. Journal of Jilin University(Engineering and Technology Edition), 2011, 41(6): 1609-1613.
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