为精确建立车辆跟驰模糊推理系统，提出采用模糊聚类分析的方法为输入、输出变量划分模糊集，并求出对应的高斯隶属度函数;引入 Takagi-Sugeno 推理方法进行车辆跟驰模糊推理与去模糊化处理;利用 NGSIM 数据对模糊推理系统和 Gipps 跟驰模型进行参数标定并评价． 结果表明:改进的建立车辆跟驰模糊推理系统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，并且所建立的模糊推理系统与 Gipps 模型相比，其误差指标的仿真精度均有较大提升．

 In order to establish the car-following fuzzy inference system (FIS) accurately，the fuzzy clustering
analysis method is adopted to divide input and output variables into various fuzzy sets，and the corresponding Gaussian membership function is determined． Then，the Takagi-Sugeno inference method is introduced to conductthe car-following fuzzy inference and the defuzzification． Finally，the fuzzy inference system and the Gippscar-following model are calibrated and evaluated by using NGSIM data． The results show that the improved method to establish the car-following fuzzy inference systemcan truly reflect the characteristics of the data itself and the driver’s psychological and physiological features，and that the established fuzzy inference system achieves a much higher simulation accuracy of error indexes in comparison with the Gipps model．