| Citation: | WANG Xu, MA Fei, LIAO Xiaoling, JIANG Peiyu, ZHANG Wei, WANG Fang. Feature Selection for Recognition of Driving Styles Based on Multi-Classification and Supervised Learning[J]. Journal of Transport Information and Safety, 2022, 40(1): 162-168. doi: 10.3963/j.jssn.1674-4861.2022.01.019
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Traffic accidents are strongly correlated with driving style, and driving style can be intuitively represented by driving behavior. In order to further advance understanding of the relationship between driving behavior and driving style, this paper explores thedifferences between driving styles and identifies factors that affect the classification. A driving-style recognition model is then proposed and evaluated. Based on the experimental data from connected vehicles, a K-means++ algorithm is proposed and used to classify data of driving behavior under different driving styles and a support vector machine-recursive feature elimination(SVC-RFE)and a random forest-recursive feature elimination(RF-RFE)algorithm are used to rank the importance of features of driving behavior. A classification model for driving styles based on neural network and the above selected features is developed. The results show that: ①when the number of selected features is set as n = 6, the correct ranking rate of both feature ranking algorithms is above 85% and the correct rate of the RF-RFEalgorithm is up to 90%.②The indicator with the highest importance in feature ranking is the maximum speed, and its difference among the three driving style groups is up to 10 m/s. ③When only the maximum speed is used as input, the accuracy of the driving-style recognition model is 86.1% and therefore, it can be concluded that maximum speed can effectively distinguish driving styles.
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