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A Recognition Method of Aggressive Driving Behavior Based on Ensemble Learning

1
College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China
2
Collaborative Innovation Center for Intelligent Green Manufacturing Technology and Equipment of Shandong Province, Qingdao 266000, China
*
Author to whom correspondence should be addressed.
Academic Editor: Felipe Jiménez
Sensors 2022, 22(2), 644; https://doi.org/10.3390/s22020644
Received: 28 November 2021 / Revised: 7 January 2022 / Accepted: 12 January 2022 / Published: 14 January 2022
(This article belongs to the Section Vehicular Sensing)
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 F1-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. View Full-Text
Keywords: aggressive driving behavior; class imbalance dataset; ensemble learning; deep learning; advanced driver assistance system aggressive driving behavior; class imbalance dataset; ensemble learning; deep learning; advanced driver assistance system
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MDPI and ACS Style

Wang, H.; Wang, X.; Han, J.; Xiang, H.; Li, H.; Zhang, Y.; Li, S. A Recognition Method of Aggressive Driving Behavior Based on Ensemble Learning. Sensors 2022, 22, 644. https://doi.org/10.3390/s22020644

AMA Style

Wang H, Wang X, Han J, Xiang H, Li H, Zhang Y, Li S. A Recognition Method of Aggressive Driving Behavior Based on Ensemble Learning. Sensors. 2022; 22(2):644. https://doi.org/10.3390/s22020644

Chicago/Turabian Style

Wang, Hanqing, Xiaoyuan Wang, Junyan Han, Hui Xiang, Hao Li, Yang Zhang, and Shangqing Li. 2022. "A Recognition Method of Aggressive Driving Behavior Based on Ensemble Learning" Sensors 22, no. 2: 644. https://doi.org/10.3390/s22020644

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