Driving risk assessment under the connected vehicle environment: a CNN-LSTM modeling approach
Connected vehicle (CV) is regarded as a typical feature of the future road transportation system. One core benefit of promoting CV is to improve traffic safety, and to achieve that, accurate driving risk assessment under Vehicle-to-Vehicle (V2V) communications is critical. There are two main differe...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Maximum Academic Press
2023-09-01
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| Series: | Digital Transportation and Safety |
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| Online Access: | https://www.maxapress.com/article/doi/10.48130/DTS-2023-0017 |
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| _version_ | 1850199846078644224 |
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| author | Yin Zheng Lei Han Jiqing Yu Rongjie Yu |
| author_facet | Yin Zheng Lei Han Jiqing Yu Rongjie Yu |
| author_sort | Yin Zheng |
| collection | DOAJ |
| description | Connected vehicle (CV) is regarded as a typical feature of the future road transportation system. One core benefit of promoting CV is to improve traffic safety, and to achieve that, accurate driving risk assessment under Vehicle-to-Vehicle (V2V) communications is critical. There are two main differences concluded by comparing driving risk assessment under the CV environment with traditional ones: (1) the CV environment provides high-resolution and multi-dimensional data, e.g., vehicle trajectory data, (2) Rare existing studies can comprehensively address the heterogeneity of the vehicle operating environment, e.g., the multiple interacting objects and the time-series variability. Hence, this study proposes a driving risk assessment framework under the CV environment. Specifically, first, a set of time-series top views was proposed to describe the CV environment data, expressing the detailed information on the vehicles surrounding the subject vehicle. Then, a hybrid CNN-LSTM model was established with the CNN component extracting the spatial interaction with multiple interacting vehicles and the LSTM component solving the time-series variability of the driving environment. It is proved that this model can reach an AUC of 0.997, outperforming the existing machine learning algorithms. This study contributes to the improvement of driving risk assessment under the CV environment. |
| format | Article |
| id | doaj-art-d1409c3e7f5e459b982932d6ee1ebb19 |
| institution | OA Journals |
| issn | 2837-7842 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | Maximum Academic Press |
| record_format | Article |
| series | Digital Transportation and Safety |
| spelling | doaj-art-d1409c3e7f5e459b982932d6ee1ebb192025-08-20T02:12:30ZengMaximum Academic PressDigital Transportation and Safety2837-78422023-09-012321121910.48130/DTS-2023-0017DTS-2023-0017Driving risk assessment under the connected vehicle environment: a CNN-LSTM modeling approachYin Zheng0Lei Han1Jiqing Yu2Rongjie Yu3The Key Laboratory of Road and Traffic Engineering, Ministry of Education, No. 4800 Cao'an Road, Shanghai 201804, ChinaThe Key Laboratory of Road and Traffic Engineering, Ministry of Education, No. 4800 Cao'an Road, Shanghai 201804, ChinaNingbo Hangzhou Bay Bridge Development Co., Ltd., No. 1 Hongqiao Road, Cixi 315300, Ningbo, ChinaThe Key Laboratory of Road and Traffic Engineering, Ministry of Education, No. 4800 Cao'an Road, Shanghai 201804, ChinaConnected vehicle (CV) is regarded as a typical feature of the future road transportation system. One core benefit of promoting CV is to improve traffic safety, and to achieve that, accurate driving risk assessment under Vehicle-to-Vehicle (V2V) communications is critical. There are two main differences concluded by comparing driving risk assessment under the CV environment with traditional ones: (1) the CV environment provides high-resolution and multi-dimensional data, e.g., vehicle trajectory data, (2) Rare existing studies can comprehensively address the heterogeneity of the vehicle operating environment, e.g., the multiple interacting objects and the time-series variability. Hence, this study proposes a driving risk assessment framework under the CV environment. Specifically, first, a set of time-series top views was proposed to describe the CV environment data, expressing the detailed information on the vehicles surrounding the subject vehicle. Then, a hybrid CNN-LSTM model was established with the CNN component extracting the spatial interaction with multiple interacting vehicles and the LSTM component solving the time-series variability of the driving environment. It is proved that this model can reach an AUC of 0.997, outperforming the existing machine learning algorithms. This study contributes to the improvement of driving risk assessment under the CV environment.https://www.maxapress.com/article/doi/10.48130/DTS-2023-0017connected vehicleconnected vehicle environmentdriving risk assessmentcnn-lstmtraffic safety |
| spellingShingle | Yin Zheng Lei Han Jiqing Yu Rongjie Yu Driving risk assessment under the connected vehicle environment: a CNN-LSTM modeling approach Digital Transportation and Safety connected vehicle connected vehicle environment driving risk assessment cnn-lstm traffic safety |
| title | Driving risk assessment under the connected vehicle environment: a CNN-LSTM modeling approach |
| title_full | Driving risk assessment under the connected vehicle environment: a CNN-LSTM modeling approach |
| title_fullStr | Driving risk assessment under the connected vehicle environment: a CNN-LSTM modeling approach |
| title_full_unstemmed | Driving risk assessment under the connected vehicle environment: a CNN-LSTM modeling approach |
| title_short | Driving risk assessment under the connected vehicle environment: a CNN-LSTM modeling approach |
| title_sort | driving risk assessment under the connected vehicle environment a cnn lstm modeling approach |
| topic | connected vehicle connected vehicle environment driving risk assessment cnn-lstm traffic safety |
| url | https://www.maxapress.com/article/doi/10.48130/DTS-2023-0017 |
| work_keys_str_mv | AT yinzheng drivingriskassessmentundertheconnectedvehicleenvironmentacnnlstmmodelingapproach AT leihan drivingriskassessmentundertheconnectedvehicleenvironmentacnnlstmmodelingapproach AT jiqingyu drivingriskassessmentundertheconnectedvehicleenvironmentacnnlstmmodelingapproach AT rongjieyu drivingriskassessmentundertheconnectedvehicleenvironmentacnnlstmmodelingapproach |