基于多分类监督学习的驾驶风格特征指标筛选

交通事故与驾驶风格具有强烈的相关性,而驾驶风格的直观体现是驾驶行为.为深入分析驾驶行为与驾驶风格的关联性,探索不同驾驶风格群体之间的差异,筛选驾驶风格分类与识别影响因素,建立驾驶风格识别模型并验证有效性.依托车联网实验数据,利用K-means++算法对驾驶员样本数据集进行驾驶风格聚类,设计支持向量机-递归特征消除(SV...

Feature Selection for Recognition of Driving Styles Based on Multi-Classification and Supervised Learning

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.