基于模糊推理的车辆换道分析研究

doi:10.3969/j.issn.1674-0696.2016.04.24

重庆交通大学学报（自然科学版） ›› 2016, Vol. 35 ›› Issue (4): 121-126.DOI: 10.3969/j.issn.1674-0696.2016.04.24

基于模糊推理的车辆换道分析研究

邱小平^1,2,3，马丽娜¹

(1.西南交通大学交通运输与物流学院，四川成都 610031; 2. 西南交通大学综合交通运输智能化国家地方联合工程实验室，四川成都 610031; 3. 综合运输四川省重点实验室，四川成都610031)

收稿日期:2015-06-10 修回日期:2015-08-14 出版日期:2016-08-20 发布日期:2016-08-20
作者简介:第一作者：邱小平（1976—），男，四川南充人，教授，博士，主要从事交通运输规划与管理方面的研究。E-mail:qxp@home.swjtu.edu.cn。
基金资助:
国家自然科学基金项目(51278429,51408509)；四川省科技厅项目(2013GZX0167,2014ZR0091)；中央高校基本业务经费项目(SWJTU11CX080)；成都市科技局项目(2014-RK00-00056-ZF)

Vehicle Lane Changing Analysis Based on Fuzzy Reasoning

QIU Xiaoping^1,2,3, MA Lina¹

(1.School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, Sichuan, P.R.China; 2. Comprehensive Intelligent Transportation National and Local Joint Engineering Laboratory, Southwest Jiaotong University, Chengdu 610031, Sichuan, P.R.China; 3. Comprehensive Transportation Key Laboratory of Sichuan Province, Chengdu 610031, Sichuan,P.R.China)

Received:2015-06-10 Revised:2015-08-14 Online:2016-08-20 Published:2016-08-20
Contact: 马丽娜（1990—），女，安徽淮北人，硕士研究生，主要从事交通流、自动驾驶方面的研究。E-mail:linamln@163.com。

摘要/Abstract

摘要： 为有效解决车辆换道行人研究中驾驶员对周围环境认知的不确定性，首次提出利用模糊推理系统对驾驶员换道行为进行分析。提出采用模糊聚类分析的方法进行输入变量的模糊集划分，求出对应的高斯隶属函数，首次引入Takagi-Sugeno推理方法进行车辆换道的模糊推理和去模糊化处理。利用NGSIM数据对建立的模糊推理进行参数标定，并进行推理结果分析，结果表明：利用模糊聚类确定隶属度函数的方法,能真实反映数据本身的特征和驾驶员的心理生理特性；而且推理结果与真实换道决策相比较时,其判断正确率高达81%，充分证明模糊推理在研究离散型推断问题中是可行的，而且此方法还可进一步应用到自动驾驶、驾驶员辅助系统的开发中。

关键词: 交通运输工程, 车辆换道, 模糊聚类, 高斯隶属度函数, Takagi-Sugeno推理方法

Abstract: To effectively solve the problem of driver’s uncertainty of surrounding perceived during his lane change, the fuzzy cluster analysis was initially applied to analyze driver’s behavior of lane changing. The fuzzy cluster analysis method was proposed to divide the fuzzy cluster after variable input to obtain corresponding Gaussian membership function. Takagi-Sugenoinference method was used in fuzzy reasoning and de-fuzzy treatment for behavior of lane changing.NGSIM data was used to calibrate the parameters of the fuzzy reasoning model established and analyze the reasoning results. The results show that: by the use of fuzzy clustering method to determine the membership function, the true data itself and driver’s psycho-physiological characteristics can be reflected and when compared with lane change decision made in reality , this inference method achieved correction rate up to 81%, which fully verified the feasibility of fuzzy inference in study of discrete issues and this method can be further used in development of automated driving, driver-aiding systems.

Key words: traffic and transportation engineering, lane changing, fuzzy clustering, gaussian membership function, Takagi Sugeno inference method

中图分类号:

U491.2

邱小平,马丽娜. 基于模糊推理的车辆换道分析研究[J]. 重庆交通大学学报（自然科学版）, 2016, 35(4): 121-126.

QIU Xiaoping, MA Lina. Vehicle Lane Changing Analysis Based on Fuzzy Reasoning[J]. Journal of Chongqing Jiaotong University(Natural Science), 2016, 35(4): 121-126.

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基于模糊推理的车辆换道分析研究

Vehicle Lane Changing Analysis Based on Fuzzy Reasoning

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