| Téléchargement | - Voir la version finale : Quantum long short-term memory-assisted optimization for efficient vehicle platooning in connected and autonomous systems (PDF, 3.1 Mio)
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| DOI | Trouver le DOI : https://doi.org/10.1109/OJCS.2024.3513237 |
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| Auteur | Rechercher : Emu, MahzabeenIdentifiant ORCID : https://orcid.org/0000-0002-0433-1873; Rechercher : Rahman, Taufiq1Identifiant ORCID : https://orcid.org/0009-0005-2774-8398; Rechercher : Choudhury, SalimurIdentifiant ORCID : https://orcid.org/0000-0002-3187-112X; Rechercher : Salomaa, KaiIdentifiant ORCID : https://orcid.org/0000-0003-4582-7477 |
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| Affiliation | - Conseil national de recherches Canada. Automobile et les transports de surface
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| Bailleur de fonds | Rechercher : Natural Sciences and Engineering Research Council of Canada; Rechercher : Doctoral Vanier Canada Graduate Scholarship; Rechercher : Office of Energy Research and Development; Rechercher : Natural Resources Canada; Rechercher : Energy-Efficient Transportation; Rechercher : National Research Council Canada |
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| Format | Texte, Article |
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| Sujet | vehicle platooning; quantum long short term memory; optimization; quantum computing; control optimization; vehicle dynamics; predictive models; long short term memory; computational modeling; safety; autonomous vehicles; stability criteria; simulation; real-time systems |
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| Résumé | Vehicle platooning, especially when dedicated to carrying goods, represents a forward-looking approach to optimizing logistics and freight transportation using autonomous vehicles. In this study, we propose to employ Quantum Long Short Term Memory (QLSTM) models to predict the vehicle dynamics of a leading vehicle of the platoon. This predictive capability allows the following vehicles to adjust their behaviours dynamically. By doing so, we aim to optimize control strategies and maintain string stability within vehicle platoons. This approach leverages the unique computational advantages of quantum computing, particularly in processing complex temporal data, potentially leading to more accurate and efficient dynamic systems in vehicular platoon infrastructure. The simulation results indicate that the QLSTM model is highly efficient by learning more information in fewer epochs compared to traditional Long Short Term Memory (LSTM) models. This efficiency contributes to minimizing control errors, enhancing the precision and reliability of vehicle dynamics in the context of autonomous vehicle platooning. This research not only enhances the predictability of autonomous vehicle platoons but also opens pathways for research into how quantum computing can be integrated into real-time dynamic systems analysis and control. |
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| Date de publication | 2024-12-09 |
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| Maison d’édition | IEEE |
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| Licence | |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 526d06df-7808-417b-a67c-39de3fdd5896 |
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| Enregistrement créé | 2025-06-30 |
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| Enregistrement modifié | 2025-11-03 |
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