1 research outputs found
A Rapid Pattern-Recognition Method for Driving Types 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 MC-SVM is an effective method to classify driving styles
with a short time, compared with SVM method.Comment: 6 pages, 9 figures, 2 tables. To be appear in 2016 American Control
Conference, Boston, MA, USA, 201