基于纯电动汽车高频数据的驾驶风格分类方法

Driving Style Classification Method Based on High-frequency Data from Pure Electric Vehicles

摘要:

基于纯电动汽车的高频运行数据选取了15项与驾驶安全有关的特征参数；通过对各特征参数的统计分析，提出了多参数组合阈值边界线进行危险驾驶行为识别，然后对比分析了两种聚类算法和4种降维方法的效果，采用t分布随机邻域嵌入（t-SNE）和高斯混合模型（GMM）组合算法建立了驾驶风格分类模型，将驾驶风格分为3种类型。在此基础上，研究了不同驾驶风格对应的车辆运行特性。该分类模型能够有效反映驾驶员的驾驶习惯，为车队管理和道路安全提供可靠的评估依据。

Abstract:

15 characteristic parameters related to driving safety were selected based on the operation data of pure electric vehicles. According to the statistics of the parameters， threshold line with multiple parameters was proposed. Then two clustering algorithms and four dimension-reduction methods were compared. Based on the classification effect， the combination algorithm of t-Distribution stochastic neighbor embedding （t-SNE） and Gaussian Mixture Model （GMM） were used to establish the driving style classification model， which was used to classify the driving style into three classes. On this basis， the vehicle operation characteristics of the three classes were compared. The classification model can effectively reflect the driving behavior and provide reliable evaluation basis for fleet management and road safety.