基于一种多分类半监督学习算法的驾驶风格分类模型

Driving Style Classification Model Based on a Multi-label Semi-supervised Learning Algorithm

基于驾驶模拟平台设计实验方案,同步采集驾驶员的驾驶操作信息和车辆状态信息,选取6个表征驾驶风格的特征参数,采用主成分分析(Principal Component Analysis,PCA)算法对多元特征参数进行特征提取,将前3个主成分作为驾驶风格识别模型的特征输入.利用K-means聚类完成样本标记工作.基于有监督支持向量机(Support Vector Machine,SVM)与多分类半监督学习算法(i MLCU)的原理,分别建立SVM与i MLCU驾驶风格识别模型,通过调节标记样本与未标记样本比例,对比使用不同样本比例训练的SVM和i MLCU模型的驾驶风格识别准确率.结果表明:相比于SVM,i MLCU表现出了更优异的驾驶风格识别能力,由此可知半监督i MLCU模型可以利用未标记样本提高模型对驾驶风格的识别能力.

This paper designs an experimental scheme based on the driving simulation platform and collects driver's operation information and vehicle status information synchronously.Six characteristic parameters are selected to recognize the driving style.The principal component analysis(PCA)algorithm is used to extract the multi-feature parameters and the first three principal components are used as the input features of the driving style recognition model.The K-means method is used for data labeling.Based on the principles of supervised support vector machine(SVM)method and inductive multi-label classification with unlabeled data(iMLCU)approach,the driving style recognition models of SVM and iMLCU are established,respectively.By adjusting the trained dataset ratios between the labeled and the unlabeled data,the accuracy of driving style recognition between the two models is compared.The results show that iMLCU has better driving style recognition than SVM.The semi-supervised iMLCU model can improve the recognition ability of driving style by using unlabeled samples.