Starting driving style recognition of electric city bus based on deep learning and CAN data
Drivers with aggressive driving style driving electric city buses with rapid response and high acceleration performance characteristics are more prone to have traffic accidents in the starting stage. It is of great importance to accurately identify the drivers with aggressive driving style for prev...
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| Format: | Article |
| Language: | English |
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Vilnius Gediminas Technical University
2024-12-01
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| Series: | Transport |
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| Online Access: | https://jest.vgtu.lt/index.php/Transport/article/view/22749 |
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| author | Dengfeng Zhao Zhijun Fu Chaohui Liu Junjian Hou Shesen Dong Yudong Zhong |
| author_facet | Dengfeng Zhao Zhijun Fu Chaohui Liu Junjian Hou Shesen Dong Yudong Zhong |
| author_sort | Dengfeng Zhao |
| collection | DOAJ |
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Drivers with aggressive driving style driving electric city buses with rapid response and high acceleration performance characteristics are more prone to have traffic accidents in the starting stage. It is of great importance to accurately identify the drivers with aggressive driving style for preventing traffic accidents of city buses. In this article, a starting driving style recognition method of electric city bus is firstly proposed based on deep learning with in-vehicle Controller Area Network (CAN) bus data. The proposed model can automatically extract the deep spatiotemporal features of multi-channel time series data and achieve end-to-end data processing with higher accuracy and generalization ability. The sample data set of driving style is established by pre-processing the collected in-vehicle CAN bus data including the status of driving and vehicle motion, the data pre-processing method includes data cleaning, normalization and sample segmentation. Data set is labelled with subjective evaluation method. The starting driving style recognition method based on Convolutional Neural Network (CNN) model is constructed. Multiple sets of convolutional layers and pooling layers are used to automatically extract the spatiotemporal characteristics of starting driving style hidden in the data such as velocity and pedal position etc. The fully connected neural network and incentive function Softmax are applied to establish the relationship mapping between driving data characteristics and the starting driving styles, which are categorized as cautious, normal and aggressive. The results show that the proposed model can accurately recognize the starting driving style of electric city bus drivers with an accuracy of 98.3%. In addition, the impact of different model structures on model performance such as accuracy and F1 scores was discussed, and the performance of the proposed model was also compared with Support Vector Machine (SVM) and random forest model. The method can be used to accurately identify drivers with aggressive starting driving style and provide references for driver’s safety education, so as to prevent accidents at the starting stage of electric city bus and reduce crash accidents.
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| format | Article |
| id | doaj-art-4ff4af9e41de411498ff03c4a0c19dee |
| institution | DOAJ |
| issn | 1648-4142 1648-3480 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Vilnius Gediminas Technical University |
| record_format | Article |
| series | Transport |
| spelling | doaj-art-4ff4af9e41de411498ff03c4a0c19dee2025-08-20T02:48:47ZengVilnius Gediminas Technical UniversityTransport1648-41421648-34802024-12-0139310.3846/transport.2024.22749Starting driving style recognition of electric city bus based on deep learning and CAN dataDengfeng Zhao0Zhijun Fu1Chaohui Liu2Junjian Hou3Shesen Dong4Yudong Zhong5Zhengzhou University of Light Industry, Zhengzhou, ChinaZhengzhou University of Light Industry, Zhengzhou, ChinaResearch Institute of Technology, Yutong Bus Co. Ltd., Zhengzhou, ChinaZhengzhou University of Light Industry, Zhengzhou, ChinaZhengzhou Senpeng Electronic Technology Co. Ltd., Zhengzhou, ChinaZhengzhou University of Light Industry, Zhengzhou, China Drivers with aggressive driving style driving electric city buses with rapid response and high acceleration performance characteristics are more prone to have traffic accidents in the starting stage. It is of great importance to accurately identify the drivers with aggressive driving style for preventing traffic accidents of city buses. In this article, a starting driving style recognition method of electric city bus is firstly proposed based on deep learning with in-vehicle Controller Area Network (CAN) bus data. The proposed model can automatically extract the deep spatiotemporal features of multi-channel time series data and achieve end-to-end data processing with higher accuracy and generalization ability. The sample data set of driving style is established by pre-processing the collected in-vehicle CAN bus data including the status of driving and vehicle motion, the data pre-processing method includes data cleaning, normalization and sample segmentation. Data set is labelled with subjective evaluation method. The starting driving style recognition method based on Convolutional Neural Network (CNN) model is constructed. Multiple sets of convolutional layers and pooling layers are used to automatically extract the spatiotemporal characteristics of starting driving style hidden in the data such as velocity and pedal position etc. The fully connected neural network and incentive function Softmax are applied to establish the relationship mapping between driving data characteristics and the starting driving styles, which are categorized as cautious, normal and aggressive. The results show that the proposed model can accurately recognize the starting driving style of electric city bus drivers with an accuracy of 98.3%. In addition, the impact of different model structures on model performance such as accuracy and F1 scores was discussed, and the performance of the proposed model was also compared with Support Vector Machine (SVM) and random forest model. The method can be used to accurately identify drivers with aggressive starting driving style and provide references for driver’s safety education, so as to prevent accidents at the starting stage of electric city bus and reduce crash accidents. https://jest.vgtu.lt/index.php/Transport/article/view/22749CAN bus datadeep learningdriving styleelectric city busrecognition |
| spellingShingle | Dengfeng Zhao Zhijun Fu Chaohui Liu Junjian Hou Shesen Dong Yudong Zhong Starting driving style recognition of electric city bus based on deep learning and CAN data Transport CAN bus data deep learning driving style electric city bus recognition |
| title | Starting driving style recognition of electric city bus based on deep learning and CAN data |
| title_full | Starting driving style recognition of electric city bus based on deep learning and CAN data |
| title_fullStr | Starting driving style recognition of electric city bus based on deep learning and CAN data |
| title_full_unstemmed | Starting driving style recognition of electric city bus based on deep learning and CAN data |
| title_short | Starting driving style recognition of electric city bus based on deep learning and CAN data |
| title_sort | starting driving style recognition of electric city bus based on deep learning and can data |
| topic | CAN bus data deep learning driving style electric city bus recognition |
| url | https://jest.vgtu.lt/index.php/Transport/article/view/22749 |
| work_keys_str_mv | AT dengfengzhao startingdrivingstylerecognitionofelectriccitybusbasedondeeplearningandcandata AT zhijunfu startingdrivingstylerecognitionofelectriccitybusbasedondeeplearningandcandata AT chaohuiliu startingdrivingstylerecognitionofelectriccitybusbasedondeeplearningandcandata AT junjianhou startingdrivingstylerecognitionofelectriccitybusbasedondeeplearningandcandata AT shesendong startingdrivingstylerecognitionofelectriccitybusbasedondeeplearningandcandata AT yudongzhong startingdrivingstylerecognitionofelectriccitybusbasedondeeplearningandcandata |