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A Recognition Method of Aggressive Driving Behavior Based on Ensemble Learning
Aggressive driving behavior (ADB) is one of the main causes of traffic accidents. The accurate recognition of ADB is the premise to timely and effectively conduct warning or intervention to the driver. There are some disadvantages, such as high miss rate and low accuracy, in the previous data-driven...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8781618/ https://www.ncbi.nlm.nih.gov/pubmed/35062603 http://dx.doi.org/10.3390/s22020644 |
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author | Wang, Hanqing Wang, Xiaoyuan Han, Junyan Xiang, Hui Li, Hao Zhang, Yang Li, Shangqing |
author_facet | Wang, Hanqing Wang, Xiaoyuan Han, Junyan Xiang, Hui Li, Hao Zhang, Yang Li, Shangqing |
author_sort | Wang, Hanqing |
collection | PubMed |
description | Aggressive driving behavior (ADB) is one of the main causes of traffic accidents. The accurate recognition of ADB is the premise to timely and effectively conduct warning or intervention to the driver. There are some disadvantages, such as high miss rate and low accuracy, in the previous data-driven recognition methods of ADB, which are caused by the problems such as the improper processing of the dataset with imbalanced class distribution and one single classifier utilized. Aiming to deal with these disadvantages, an ensemble learning-based recognition method of ADB is proposed in this paper. First, the majority class in the dataset is grouped employing the self-organizing map (SOM) and then are combined with the minority class to construct multiple class balance datasets. Second, three deep learning methods, including convolutional neural networks (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU), are employed to build the base classifiers for the class balance datasets. Finally, the ensemble classifiers are combined by the base classifiers according to 10 different rules, and then trained and verified using a multi-source naturalistic driving dataset acquired by the integrated experiment vehicle. The results suggest that in terms of the recognition of ADB, the ensemble learning method proposed in this research achieves better performance in accuracy, recall, and F(1)-score than the aforementioned typical deep learning methods. Among the ensemble classifiers, the one based on the LSTM and the Product Rule has the optimal performance, and the other one based on the LSTM and the Sum Rule has the suboptimal performance. |
format | Online Article Text |
id | pubmed-8781618 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87816182022-01-22 A Recognition Method of Aggressive Driving Behavior Based on Ensemble Learning Wang, Hanqing Wang, Xiaoyuan Han, Junyan Xiang, Hui Li, Hao Zhang, Yang Li, Shangqing Sensors (Basel) Article Aggressive driving behavior (ADB) is one of the main causes of traffic accidents. The accurate recognition of ADB is the premise to timely and effectively conduct warning or intervention to the driver. There are some disadvantages, such as high miss rate and low accuracy, in the previous data-driven recognition methods of ADB, which are caused by the problems such as the improper processing of the dataset with imbalanced class distribution and one single classifier utilized. Aiming to deal with these disadvantages, an ensemble learning-based recognition method of ADB is proposed in this paper. First, the majority class in the dataset is grouped employing the self-organizing map (SOM) and then are combined with the minority class to construct multiple class balance datasets. Second, three deep learning methods, including convolutional neural networks (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU), are employed to build the base classifiers for the class balance datasets. Finally, the ensemble classifiers are combined by the base classifiers according to 10 different rules, and then trained and verified using a multi-source naturalistic driving dataset acquired by the integrated experiment vehicle. The results suggest that in terms of the recognition of ADB, the ensemble learning method proposed in this research achieves better performance in accuracy, recall, and F(1)-score than the aforementioned typical deep learning methods. Among the ensemble classifiers, the one based on the LSTM and the Product Rule has the optimal performance, and the other one based on the LSTM and the Sum Rule has the suboptimal performance. MDPI 2022-01-14 /pmc/articles/PMC8781618/ /pubmed/35062603 http://dx.doi.org/10.3390/s22020644 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wang, Hanqing Wang, Xiaoyuan Han, Junyan Xiang, Hui Li, Hao Zhang, Yang Li, Shangqing A Recognition Method of Aggressive Driving Behavior Based on Ensemble Learning |
title | A Recognition Method of Aggressive Driving Behavior Based on Ensemble Learning |
title_full | A Recognition Method of Aggressive Driving Behavior Based on Ensemble Learning |
title_fullStr | A Recognition Method of Aggressive Driving Behavior Based on Ensemble Learning |
title_full_unstemmed | A Recognition Method of Aggressive Driving Behavior Based on Ensemble Learning |
title_short | A Recognition Method of Aggressive Driving Behavior Based on Ensemble Learning |
title_sort | recognition method of aggressive driving behavior based on ensemble learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8781618/ https://www.ncbi.nlm.nih.gov/pubmed/35062603 http://dx.doi.org/10.3390/s22020644 |
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