A spatiotemporal learning approach to safety‐oriented individualized driving risk assessment in a vehicle‐to‐everything (V2X) environment
Abstract Advances in real‐time basic safety message (BSM) data from sensor‐equipped vehicles have created new opportunities for driving risk assessments. This paper presents a machine learning approach using BSM data to provide fine‐grained risk assessments, focusing on safety‐critical events (SCEs)...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Intelligent Transport Systems |
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| Online Access: | https://doi.org/10.1049/itr2.12584 |
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| author | Jing Li Xuantong Wang Tong Zhang |
| author_facet | Jing Li Xuantong Wang Tong Zhang |
| author_sort | Jing Li |
| collection | DOAJ |
| description | Abstract Advances in real‐time basic safety message (BSM) data from sensor‐equipped vehicles have created new opportunities for driving risk assessments. This paper presents a machine learning approach using BSM data to provide fine‐grained risk assessments, focusing on safety‐critical events (SCEs) related to driving profiles, vehicle states, and road conditions. This approach formulates a bi‐level risk indicator: one level measures the observable frequency of SCEs, while the other estimates their likelihood. The coarse level calculates risk scores by classifying driving profiles as normal or risky based on SCE frequency. The fine level refines these scores by comparing normal and risky profiles using key features from a feature learning model. This combined system accounts for recent driving behaviours and road/weather conditions within a vehicle‐to‐everything (V2X) environment, addressing high data dimensionality and imbalance. A comprehensive case study using 1 year of data from pilot V2X infrastructure in Tampa, Florida, demonstrates the efficacy of this approach, showing practical applications of the SCE‐based risk indicator and combinatorial feature learning while also highlighting the real‐world utility of the assessment method in providing a detailed and actionable view of driving risk based on V2X information. |
| format | Article |
| id | doaj-art-b8cdf451b2194e018c6548da1dc9d613 |
| institution | OA Journals |
| issn | 1751-956X 1751-9578 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Intelligent Transport Systems |
| spelling | doaj-art-b8cdf451b2194e018c6548da1dc9d6132025-08-20T02:19:34ZengWileyIET Intelligent Transport Systems1751-956X1751-95782024-12-0118122459248410.1049/itr2.12584A spatiotemporal learning approach to safety‐oriented individualized driving risk assessment in a vehicle‐to‐everything (V2X) environmentJing Li0Xuantong Wang1Tong Zhang2Department of Geography and the Environment University of Denver Colorado USADepartment of Geosciences Texas Tech University Texas USAState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing Wuhan University Wuhan ChinaAbstract Advances in real‐time basic safety message (BSM) data from sensor‐equipped vehicles have created new opportunities for driving risk assessments. This paper presents a machine learning approach using BSM data to provide fine‐grained risk assessments, focusing on safety‐critical events (SCEs) related to driving profiles, vehicle states, and road conditions. This approach formulates a bi‐level risk indicator: one level measures the observable frequency of SCEs, while the other estimates their likelihood. The coarse level calculates risk scores by classifying driving profiles as normal or risky based on SCE frequency. The fine level refines these scores by comparing normal and risky profiles using key features from a feature learning model. This combined system accounts for recent driving behaviours and road/weather conditions within a vehicle‐to‐everything (V2X) environment, addressing high data dimensionality and imbalance. A comprehensive case study using 1 year of data from pilot V2X infrastructure in Tampa, Florida, demonstrates the efficacy of this approach, showing practical applications of the SCE‐based risk indicator and combinatorial feature learning while also highlighting the real‐world utility of the assessment method in providing a detailed and actionable view of driving risk based on V2X information.https://doi.org/10.1049/itr2.12584artificial intelligencefeature extractionintelligent transportation systemslearning (artificial intelligence)risk analysis |
| spellingShingle | Jing Li Xuantong Wang Tong Zhang A spatiotemporal learning approach to safety‐oriented individualized driving risk assessment in a vehicle‐to‐everything (V2X) environment IET Intelligent Transport Systems artificial intelligence feature extraction intelligent transportation systems learning (artificial intelligence) risk analysis |
| title | A spatiotemporal learning approach to safety‐oriented individualized driving risk assessment in a vehicle‐to‐everything (V2X) environment |
| title_full | A spatiotemporal learning approach to safety‐oriented individualized driving risk assessment in a vehicle‐to‐everything (V2X) environment |
| title_fullStr | A spatiotemporal learning approach to safety‐oriented individualized driving risk assessment in a vehicle‐to‐everything (V2X) environment |
| title_full_unstemmed | A spatiotemporal learning approach to safety‐oriented individualized driving risk assessment in a vehicle‐to‐everything (V2X) environment |
| title_short | A spatiotemporal learning approach to safety‐oriented individualized driving risk assessment in a vehicle‐to‐everything (V2X) environment |
| title_sort | spatiotemporal learning approach to safety oriented individualized driving risk assessment in a vehicle to everything v2x environment |
| topic | artificial intelligence feature extraction intelligent transportation systems learning (artificial intelligence) risk analysis |
| url | https://doi.org/10.1049/itr2.12584 |
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