Driving Style Classification Using a Semisupervised Support Vector Machine

Abstract

Supervised learning approaches are widely used for driving style classification; however, they often require a large amount of labeled training data, which is usually scarce in a real-world setting. Moreover, it is time-consuming to manually label huge amounts of driving data due to uncertainties of driver behavior and variances among the data analysts. To address this problem, a semisupervised approach, a semisupervised support vector machine (S3VM), is employed to classify drivers into aggressive and normal styles based on a few labeled data points. First, a few data clusters are selected and manually labeled using a k-means clustering method. Then, a specific differentiable surrogate of a loss function is developed, which makes it feasible to use standard optimization tools to solve the nonconvex optimization problem. One of the most popular quasi-Newton algorithms is then used to assign the optimal label to all of the training data. Finally, we compare the S3VM method with a support vector machine method for classifying driving styles from different amounts of labeled data. Experiments show that the S3VM method can improve the classification accuracy by about 10% and reduce the labeling effort by using only a few labeled data clusters among huge amounts of unlabeled data.