Intelligent Method for Identifying Driving Risk Based on V2V Multisource Big Data
Risky driving behavior is a major cause of traffic conflicts, which can develop into road traffic accidents, making the timely and accurate identification of such behavior essential to road safety. A platform was therefore established for analyzing the driving behavior of 20 professional drivers in...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/1801273 |
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| _version_ | 1832554511017705472 |
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| author | Jinshuan Peng Yiming Shao |
| author_facet | Jinshuan Peng Yiming Shao |
| author_sort | Jinshuan Peng |
| collection | DOAJ |
| description | Risky driving behavior is a major cause of traffic conflicts, which can develop into road traffic accidents, making the timely and accurate identification of such behavior essential to road safety. A platform was therefore established for analyzing the driving behavior of 20 professional drivers in field tests, in which overclose car following and lane departure were used as typical risky driving behaviors. Characterization parameters for identification were screened and used to determine threshold values and an appropriate time window for identification. A neural network-Bayesian filter identification model was established and data samples were selected to identify risky driving behavior and evaluate the identification efficiency of the model. The results obtained indicated a successful identification rate of 83.6% when the neural network model was solely used to identify risky driving behavior, but this could be increased to 92.46% once corrected by the Bayesian filter. This has important theoretical and practical significance in relation to evaluating the efficiency of existing driver assist systems, as well as the development of future intelligent driving systems. |
| format | Article |
| id | doaj-art-9b7d9a3dde7a430684451574b7248eda |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9b7d9a3dde7a430684451574b7248eda2025-02-03T05:51:19ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/18012731801273Intelligent Method for Identifying Driving Risk Based on V2V Multisource Big DataJinshuan Peng0Yiming Shao1School of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, ChinaSchool of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, ChinaRisky driving behavior is a major cause of traffic conflicts, which can develop into road traffic accidents, making the timely and accurate identification of such behavior essential to road safety. A platform was therefore established for analyzing the driving behavior of 20 professional drivers in field tests, in which overclose car following and lane departure were used as typical risky driving behaviors. Characterization parameters for identification were screened and used to determine threshold values and an appropriate time window for identification. A neural network-Bayesian filter identification model was established and data samples were selected to identify risky driving behavior and evaluate the identification efficiency of the model. The results obtained indicated a successful identification rate of 83.6% when the neural network model was solely used to identify risky driving behavior, but this could be increased to 92.46% once corrected by the Bayesian filter. This has important theoretical and practical significance in relation to evaluating the efficiency of existing driver assist systems, as well as the development of future intelligent driving systems.http://dx.doi.org/10.1155/2018/1801273 |
| spellingShingle | Jinshuan Peng Yiming Shao Intelligent Method for Identifying Driving Risk Based on V2V Multisource Big Data Complexity |
| title | Intelligent Method for Identifying Driving Risk Based on V2V Multisource Big Data |
| title_full | Intelligent Method for Identifying Driving Risk Based on V2V Multisource Big Data |
| title_fullStr | Intelligent Method for Identifying Driving Risk Based on V2V Multisource Big Data |
| title_full_unstemmed | Intelligent Method for Identifying Driving Risk Based on V2V Multisource Big Data |
| title_short | Intelligent Method for Identifying Driving Risk Based on V2V Multisource Big Data |
| title_sort | intelligent method for identifying driving risk based on v2v multisource big data |
| url | http://dx.doi.org/10.1155/2018/1801273 |
| work_keys_str_mv | AT jinshuanpeng intelligentmethodforidentifyingdrivingriskbasedonv2vmultisourcebigdata AT yimingshao intelligentmethodforidentifyingdrivingriskbasedonv2vmultisourcebigdata |