A rapid pattern recognition method for driving styles using clustering based support vector machines
A rapid pattern recognition approach to characterize driver's curve negotiating behavior is proposed. To shorten the recognition time and improve the recognition of driving styles, a k means clustering based support vector machine (kMC SVM) method is developed and used for classifying drivers into two types: aggressive and moderate. First, vehicle speed and throttle opening are treated as the feature parameters to reflect the driving styles. Second, to discriminate driver curve negotiating behaviors and reduce the number of support vectors, the k means clustering method is used to extract and gather the two types of driving data and shorten the recognition time. Then, based on the clustering results, a support vector machine approach is utilized to generate the hyperplane for judging and predicting to which types the human driver are subject. Lastly, to verify the validity of the kMC SVM method, a cross validation experiment is designed and conducted. The research results show that the kMC SVM is an effective method to classify driving styles with a short time, compared with SVM method.
2016 American Control Conference (ACC)