Review of driver behaviour modelling for highway on‐ramp merging
Abstract Autonomous driving is an exciting research field that has received growing attention in recent years. One of the most challenging and safety‐critical driving situations is highway on‐ramp merging. Most decision‐making strategies that perform highway on‐ramp merging are designed, firstly, to...
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| Main Authors: | , |
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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/itr2.12572 |
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| _version_ | 1850114205004333056 |
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| author | Zine el abidine Kherroubi Samir Aknine |
| author_facet | Zine el abidine Kherroubi Samir Aknine |
| author_sort | Zine el abidine Kherroubi |
| collection | DOAJ |
| description | Abstract Autonomous driving is an exciting research field that has received growing attention in recent years. One of the most challenging and safety‐critical driving situations is highway on‐ramp merging. Most decision‐making strategies that perform highway on‐ramp merging are designed, firstly, to reduce the risk of crashes and improve the safety metrics. However, even with the development of such advanced driving systems, human drivers will still be involved in road traffic. Human drivers have various driving styles and different reactions to other traffic participants on the highway on‐ramp. Understanding driver behaviors is essential for designing safe and efficient real‐world driving strategies. Therefore, this paper provides a unique systematic review of existing techniques for modelling driver behaviors at highway on‐ramps, which are critical locations for traffic safety and efficiency. The novelty of this review is that it proposes a new classification of current state‐of‐the art techniques. Each category of techniques involves a unique paradigm. For each category of approaches, fundamental concepts are examined together with their challenges and limitations, and an overview on practical implementation. Furthermore, and based on the classification and chronological order, current research trend is identified, i.e. “data‐driven approaches”. Some future research avenues and disparities are also discussed. |
| format | Article |
| id | doaj-art-3a72015c557643eaa93b155f8135256a |
| 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-3a72015c557643eaa93b155f8135256a2025-08-20T02:36:58ZengWileyIET Intelligent Transport Systems1751-956X1751-95782024-12-0118S12793281310.1049/itr2.12572Review of driver behaviour modelling for highway on‐ramp mergingZine el abidine Kherroubi0Samir Aknine1Secure Systems Research Center Technology Innovation Institute (TII) Masdar City, Abu Dhabi United Arab EmiratesLIRIS Laboratory Claude Bernard University Lyon 1 Villeurbanne FranceAbstract Autonomous driving is an exciting research field that has received growing attention in recent years. One of the most challenging and safety‐critical driving situations is highway on‐ramp merging. Most decision‐making strategies that perform highway on‐ramp merging are designed, firstly, to reduce the risk of crashes and improve the safety metrics. However, even with the development of such advanced driving systems, human drivers will still be involved in road traffic. Human drivers have various driving styles and different reactions to other traffic participants on the highway on‐ramp. Understanding driver behaviors is essential for designing safe and efficient real‐world driving strategies. Therefore, this paper provides a unique systematic review of existing techniques for modelling driver behaviors at highway on‐ramps, which are critical locations for traffic safety and efficiency. The novelty of this review is that it proposes a new classification of current state‐of‐the art techniques. Each category of techniques involves a unique paradigm. For each category of approaches, fundamental concepts are examined together with their challenges and limitations, and an overview on practical implementation. Furthermore, and based on the classification and chronological order, current research trend is identified, i.e. “data‐driven approaches”. Some future research avenues and disparities are also discussed.https://doi.org/10.1049/itr2.12572autonomous drivingbehavioural scienceshuman factorslearning (artificial intelligence) |
| spellingShingle | Zine el abidine Kherroubi Samir Aknine Review of driver behaviour modelling for highway on‐ramp merging IET Intelligent Transport Systems autonomous driving behavioural sciences human factors learning (artificial intelligence) |
| title | Review of driver behaviour modelling for highway on‐ramp merging |
| title_full | Review of driver behaviour modelling for highway on‐ramp merging |
| title_fullStr | Review of driver behaviour modelling for highway on‐ramp merging |
| title_full_unstemmed | Review of driver behaviour modelling for highway on‐ramp merging |
| title_short | Review of driver behaviour modelling for highway on‐ramp merging |
| title_sort | review of driver behaviour modelling for highway on ramp merging |
| topic | autonomous driving behavioural sciences human factors learning (artificial intelligence) |
| url | https://doi.org/10.1049/itr2.12572 |
| work_keys_str_mv | AT zineelabidinekherroubi reviewofdriverbehaviourmodellingforhighwayonrampmerging AT samiraknine reviewofdriverbehaviourmodellingforhighwayonrampmerging |