Driving Style Recognition under Connected Circumstance Using a Supervised Hierarchical Bayesian Model
In recent years, the automated driving system has been known to be one of the most popular research topics of artificial intelligence (AI) and intelligent transportation system (ITS). The journey experience on automated vehicles and the intelligent automated driving system could be improved by indiv...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/6687378 |
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| _version_ | 1832565270218014720 |
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| author | Depeng Chen Zhijun Chen Yishi Zhang Xu Qu Mingyang Zhang Chaozhong Wu |
| author_facet | Depeng Chen Zhijun Chen Yishi Zhang Xu Qu Mingyang Zhang Chaozhong Wu |
| author_sort | Depeng Chen |
| collection | DOAJ |
| description | In recent years, the automated driving system has been known to be one of the most popular research topics of artificial intelligence (AI) and intelligent transportation system (ITS). The journey experience on automated vehicles and the intelligent automated driving system could be improved by individualization driving understanding. Although previous studies have proposed methods for driving styles understanding, the individualization driving classification has not been addressed thoroughly. Therefore, in this study, a supervised method is proposed to understand driving behavioral structure and the latent driving styles by incorporating the prior knowledge. Firstly, a novel method is established for driving behavioral encoding and raw driving data mining. Then, the Labeled Latent Dirichlet Allocation (LLDA) is proposed to understand the latent driving styles from individual driving with driving behaviors. Finally, the Safety Pilot Model Deployment (SPMD) data are used to validate the performance of the proposed model. Experimental results show that the proposed model uncovers latent driving styles effectively and shows good agreement to real situations, which provides theoretical guidance on driving behavior recognition for better individual experience on automated driving vehicles. |
| format | Article |
| id | doaj-art-eafb3021af8746b7b29e20615e73a7f0 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-eafb3021af8746b7b29e20615e73a7f02025-02-03T01:08:52ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/66873786687378Driving Style Recognition under Connected Circumstance Using a Supervised Hierarchical Bayesian ModelDepeng Chen0Zhijun Chen1Yishi Zhang2Xu Qu3Mingyang Zhang4Chaozhong Wu5Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, ChinaIntelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, ChinaSchool of Management, Wuhan University of Technology, Wuhan, ChinaSchool of Transportation, Southeast University, Nanjing, ChinaSchool of Engineering, Aalto University, Espoo, FinlandIntelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, ChinaIn recent years, the automated driving system has been known to be one of the most popular research topics of artificial intelligence (AI) and intelligent transportation system (ITS). The journey experience on automated vehicles and the intelligent automated driving system could be improved by individualization driving understanding. Although previous studies have proposed methods for driving styles understanding, the individualization driving classification has not been addressed thoroughly. Therefore, in this study, a supervised method is proposed to understand driving behavioral structure and the latent driving styles by incorporating the prior knowledge. Firstly, a novel method is established for driving behavioral encoding and raw driving data mining. Then, the Labeled Latent Dirichlet Allocation (LLDA) is proposed to understand the latent driving styles from individual driving with driving behaviors. Finally, the Safety Pilot Model Deployment (SPMD) data are used to validate the performance of the proposed model. Experimental results show that the proposed model uncovers latent driving styles effectively and shows good agreement to real situations, which provides theoretical guidance on driving behavior recognition for better individual experience on automated driving vehicles.http://dx.doi.org/10.1155/2021/6687378 |
| spellingShingle | Depeng Chen Zhijun Chen Yishi Zhang Xu Qu Mingyang Zhang Chaozhong Wu Driving Style Recognition under Connected Circumstance Using a Supervised Hierarchical Bayesian Model Journal of Advanced Transportation |
| title | Driving Style Recognition under Connected Circumstance Using a Supervised Hierarchical Bayesian Model |
| title_full | Driving Style Recognition under Connected Circumstance Using a Supervised Hierarchical Bayesian Model |
| title_fullStr | Driving Style Recognition under Connected Circumstance Using a Supervised Hierarchical Bayesian Model |
| title_full_unstemmed | Driving Style Recognition under Connected Circumstance Using a Supervised Hierarchical Bayesian Model |
| title_short | Driving Style Recognition under Connected Circumstance Using a Supervised Hierarchical Bayesian Model |
| title_sort | driving style recognition under connected circumstance using a supervised hierarchical bayesian model |
| url | http://dx.doi.org/10.1155/2021/6687378 |
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