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

LI Mingjun, ZHANG Zhenghao, SONG Xiaolin, CAO Haotian, YI Binlin


 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.


Keywords:  driving style,  principal component analysis,  K-means clustering,  support vector machines,  multi-label semi-supervised classification with unlabeled data

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