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Application of conditional generative adversarial network to multi-step car-following modeling
Car-following modeling is essential in the longitudinal control for connected and autonomous vehicles (CAVs). Considering the advantage of the generative adversarial network (GAN) in capturing realistic data distribution, this paper applies conditional GAN (CGAN) to car-following modeling. The gener...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10076580/ https://www.ncbi.nlm.nih.gov/pubmed/37033415 http://dx.doi.org/10.3389/fnbot.2023.1148892 |
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author | Ma, Lijing Qu, Shiru |
author_facet | Ma, Lijing Qu, Shiru |
author_sort | Ma, Lijing |
collection | PubMed |
description | Car-following modeling is essential in the longitudinal control for connected and autonomous vehicles (CAVs). Considering the advantage of the generative adversarial network (GAN) in capturing realistic data distribution, this paper applies conditional GAN (CGAN) to car-following modeling. The generator is elaborately designed with a sequence-to-sequence structure to reflect the decision-making process of human driving behavior. The proposed model is trained and tested based on the empirical dataset, and it is compared with a supervised learning model and a mathematical model. Numerical simulations are conducted to verify the model's performance, especially in the condition of mixed traffic flow. The comparison result shows that the CGAN model outperforms others in trajectory reproduction, indicating it can effectively imitate human driving behavior. The simulation results suggest that the introduction of CGAN-based CAVs improves the stability and efficiency of the mixed traffic flow. |
format | Online Article Text |
id | pubmed-10076580 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100765802023-04-07 Application of conditional generative adversarial network to multi-step car-following modeling Ma, Lijing Qu, Shiru Front Neurorobot Neuroscience Car-following modeling is essential in the longitudinal control for connected and autonomous vehicles (CAVs). Considering the advantage of the generative adversarial network (GAN) in capturing realistic data distribution, this paper applies conditional GAN (CGAN) to car-following modeling. The generator is elaborately designed with a sequence-to-sequence structure to reflect the decision-making process of human driving behavior. The proposed model is trained and tested based on the empirical dataset, and it is compared with a supervised learning model and a mathematical model. Numerical simulations are conducted to verify the model's performance, especially in the condition of mixed traffic flow. The comparison result shows that the CGAN model outperforms others in trajectory reproduction, indicating it can effectively imitate human driving behavior. The simulation results suggest that the introduction of CGAN-based CAVs improves the stability and efficiency of the mixed traffic flow. Frontiers Media S.A. 2023-03-23 /pmc/articles/PMC10076580/ /pubmed/37033415 http://dx.doi.org/10.3389/fnbot.2023.1148892 Text en Copyright © 2023 Ma and Qu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Ma, Lijing Qu, Shiru Application of conditional generative adversarial network to multi-step car-following modeling |
title | Application of conditional generative adversarial network to multi-step car-following modeling |
title_full | Application of conditional generative adversarial network to multi-step car-following modeling |
title_fullStr | Application of conditional generative adversarial network to multi-step car-following modeling |
title_full_unstemmed | Application of conditional generative adversarial network to multi-step car-following modeling |
title_short | Application of conditional generative adversarial network to multi-step car-following modeling |
title_sort | application of conditional generative adversarial network to multi-step car-following modeling |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10076580/ https://www.ncbi.nlm.nih.gov/pubmed/37033415 http://dx.doi.org/10.3389/fnbot.2023.1148892 |
work_keys_str_mv | AT malijing applicationofconditionalgenerativeadversarialnetworktomultistepcarfollowingmodeling AT qushiru applicationofconditionalgenerativeadversarialnetworktomultistepcarfollowingmodeling |