Collaborative driving style classification method enabled by majority voting ensemble learning for enhancing classification performance.

Abstract

The classification of driving styles plays a fundamental role in evaluating drivers' driving behaviors, which is of great significance to traffic safety. However, it still suffers from various challenges, including the insufficient accuracy of the model, the large amount of training parameters, the instability of classification results, and some others. To evaluate the driving behaviors accurately and efficiently, and to study the differences of driving behaviors among various vehicle drivers, a collaborative driving style classification method, which is enabled by ensemble learning and divided into pre-classification and classification, is proposed in this paper. In the pre-classification process, various clustering algorithms are utilized compositely to label some typical initial data with specific labels as aggressive, stable and conservative. Then, in the classification process, other unlabeled data can be classified accurately and efficiently by the majority voting ensemble learning method incorporating three different conventional classifiers. The availability and efficiency of the proposed method are demonstrated through some simulation experiments, in which the proposed collaborative classification method achieves quite good and stable performance on driving style classification. Particularly, compared with some other similar classification methods, the evaluation indicators of the proposed method, including accuracy, precision, recall and F-measure, are improved by 1.49%, 2.90%, 5.32% and 4.49% respectively, making it the best overall performance. Therefore, the proposed method is much preferred for the autonomous driving and usage-based insurance.