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

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,...

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Autores principales:	Yi Guo, Xiaolan Wang, Yongmao Huang, Liang Xu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/362e75d28a2940e5b74f2410e72d851a
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spelling	oai:doaj.org-article:362e75d28a2940e5b74f2410e72d851a2021-12-02T20:06:51ZCollaborative driving style classification method enabled by majority voting ensemble learning for enhancing classification performance.1932-620310.1371/journal.pone.0254047https://doaj.org/article/362e75d28a2940e5b74f2410e72d851a2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254047https://doaj.org/toc/1932-6203The 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.Yi GuoXiaolan WangYongmao HuangLiang XuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254047 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yi Guo Xiaolan Wang Yongmao Huang Liang Xu Collaborative driving style classification method enabled by majority voting ensemble learning for enhancing classification performance.
description	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.
format	article
author	Yi Guo Xiaolan Wang Yongmao Huang Liang Xu
author_facet	Yi Guo Xiaolan Wang Yongmao Huang Liang Xu
author_sort	Yi Guo
title	Collaborative driving style classification method enabled by majority voting ensemble learning for enhancing classification performance.
title_short	Collaborative driving style classification method enabled by majority voting ensemble learning for enhancing classification performance.
title_full	Collaborative driving style classification method enabled by majority voting ensemble learning for enhancing classification performance.
title_fullStr	Collaborative driving style classification method enabled by majority voting ensemble learning for enhancing classification performance.
title_full_unstemmed	Collaborative driving style classification method enabled by majority voting ensemble learning for enhancing classification performance.
title_sort	collaborative driving style classification method enabled by majority voting ensemble learning for enhancing classification performance.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/362e75d28a2940e5b74f2410e72d851a
work_keys_str_mv	AT yiguo collaborativedrivingstyleclassificationmethodenabledbymajorityvotingensemblelearningforenhancingclassificationperformance AT xiaolanwang collaborativedrivingstyleclassificationmethodenabledbymajorityvotingensemblelearningforenhancingclassificationperformance AT yongmaohuang collaborativedrivingstyleclassificationmethodenabledbymajorityvotingensemblelearningforenhancingclassificationperformance AT liangxu collaborativedrivingstyleclassificationmethodenabledbymajorityvotingensemblelearningforenhancingclassificationperformance
_version_	1718375295716687872

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

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