A Rapid Pattern-Recognition Method for Driving Types Using Clustering-Based Support Vector Machines

TL;DR

This paper introduces a fast pattern-recognition method combining k-means clustering and support vector machines to classify drivers as aggressive or moderate based on vehicle data, enhancing recognition speed and accuracy.

Contribution

The study develops a novel kMC-SVM approach that reduces recognition time and improves classification of driving styles compared to traditional SVM methods.

Findings

kMC-SVM effectively classifies driving styles quickly.

The method outperforms standard SVM in recognition speed.

High accuracy in distinguishing aggressive and moderate drivers.

Abstract

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 $ k $MC-SVM is an effective method to classify driving styles with a short time, compared with SVM method.