A Learning-Based Driving Style Classification Approach for Intelligent Vehicles

Driving behavior is crucial to the energy consumption analysis of electric vehicles. This paper proposes an unsupervised learning method to classify driving behavior for three typical road conditions. First, three specific road conditions are selected from the open access data, including characteristic information such as speed and acceleration. Besides, the characteristic data is processed, so each distinct value has the same weight. Second, two unsupervised learning clustering algorithms are introduced and compared in typical working conditions. Finally, the clustering results under three working conditions are obtained. Specifically, we can classify driving styles in high-speed conditions into aggressive, standard, and calm; besides, the classification method of K-medoids is more advantageous. In intersection conditions, driving styles are usually divided into standard and calm. Considering the calculation time and other factors, the K-means algorithm shows superior effects compared to the K-medoids algorithm. The driving style can be divided into standard and calm in campus conditions. In this case, K-medoids have a more significant advantage. The research results have implications for the classification of driving styles under different road conditions.