针对自适应巡航的驾驶人风格辨识

车辆进入自适应巡航工况下行驶时,不同风格的驾驶人会对自适应巡航控制系统(Adaptive Cruise Control,ACC)有不同的需求。文章首先通过对不同驾驶人在9种跟随试验下获取的实验数据分析,选取表征驾驶人风格的驾驶特征参数;其次对所有驾驶人驾驶特征参数利用K-mean算法聚类分析,将驾驶人三类,并利用BP神经网络建立辨识模型对驾驶人风格进行辨识。结果表明;文章提出的方法可以较高的准确率对驾驶人风格进行分类,提高自适应巡航系统适应驾驶人的能力。

Driver Style Recognition for Adaptive Cruise

Drivers of different styles will have different requirements for the adaptive cruise control(ACC)system when the vehicle enters the adaptive cruise mode.This paper analyzes the following data of different drivers at different speeds,and selects driving characteristic parameters that characterize the driver's style.The K-mean algorithm cluster analysis is used for all drivers'driving characteristic parameters,and the three types of drivers are used to establish an identification model to identify the driver's style using BP neural network.The results show that the method proposed in this paper can classify the driver's style with higher accuracy and improve the adaptive cruise system's ability to adapt to the driver.