A Physics-Informed Generative Car-Following Model for Connected Autonomous Vehicles

This paper proposes a novel hybrid car-following model: the physics-informed conditional generative adversarial network (PICGAN), designed to enhance multi-step car-following modeling in mixed traffic flow scenarios. This hybrid model leverages the strengths of both physics-based and deep-learning-b...

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Detalles Bibliográficos
Autores principales: Ma, Lijing, Qu, Shiru, Song, Lijun, Zhang, Zhiteng, Ren, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378484/
https://www.ncbi.nlm.nih.gov/pubmed/37509998
http://dx.doi.org/10.3390/e25071050

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378484/
https://www.ncbi.nlm.nih.gov/pubmed/37509998
http://dx.doi.org/10.3390/e25071050

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