基于模糊推理的车辆换道分析研究

Vehicle Lane Changing Analysis Based on Fuzzy Reasoning

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。

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-Suge- noinference 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-physio- logical 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.