基于TSK模型的车辆跟驰模型

Car-Following Model Based on TSK Model

为解决传统的基于模糊推理的车辆跟驰模型参数校准精度差的问题,提出了基于TSK模型的车辆跟驰模型.采用改进的基于遗传算法的TSK模型辨识方法,对分组中数据点不足的情况作了特别处理,通过GPS采集跑车实验数据,根据实测数据构建并验证TSK模型.实验结果和理论分析吻合较好,模型精度提高了一个数量级,表明TSK模型用于车辆跟驰模型是可行的.

In order to improve the calibration accuracy of the traditional car-following model based on fuzzy reasoning, this paper proposes a new model based on the TSK model. In this model, an improved genetic algorithm-based learning algorithm is adopted for the identification of TSK model, and the situation with insufficient sub-group data point is specially considered. Moreover, some real car-following data are collected by GPS for the establishment and validation of the TSK model. It is found that the experimental results accord well with the theoretical ones, and that the accuracy increases by one order of magnitude, meaning that the application of TSK model to the car-following model is feasible.