Vehicle trajectory prediction and generation using LSTM models and GANs.

Vehicles' trajectory prediction is a topic with growing interest in recent years, as there are applications in several domains ranging from autonomous driving to traffic congestion prediction and urban planning. Predicting trajectories starting from Floating Car Data (FCD) is a complex task tha...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Luca Rossi, Andrea Ajmar, Marina Paolanti, Roberto Pierdicca
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e61d3f56275b4caf8ed46b5374061374
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e61d3f56275b4caf8ed46b5374061374
record_format	dspace
spelling	oai:doaj.org-article:e61d3f56275b4caf8ed46b53740613742021-12-02T20:09:46ZVehicle trajectory prediction and generation using LSTM models and GANs.1932-620310.1371/journal.pone.0253868https://doaj.org/article/e61d3f56275b4caf8ed46b53740613742021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253868https://doaj.org/toc/1932-6203Vehicles' trajectory prediction is a topic with growing interest in recent years, as there are applications in several domains ranging from autonomous driving to traffic congestion prediction and urban planning. Predicting trajectories starting from Floating Car Data (FCD) is a complex task that comes with different challenges, namely Vehicle to Infrastructure (V2I) interaction, Vehicle to Vehicle (V2V) interaction, multimodality, and generalizability. These challenges, especially, have not been completely explored by state-of-the-art works. In particular, multimodality and generalizability have been neglected the most, and this work attempts to fill this gap by proposing and defining new datasets, metrics, and methods to help understand and predict vehicle trajectories. We propose and compare Deep Learning models based on Long Short-Term Memory and Generative Adversarial Network architectures; in particular, our GAN-3 model can be used to generate multiple predictions in multimodal scenarios. These approaches are evaluated with our newly proposed error metrics N-ADE and N-FDE, which normalize some biases in the standard Average Displacement Error (ADE) and Final Displacement Error (FDE) metrics. Experiments have been conducted using newly collected datasets in four large Italian cities (Rome, Milan, Naples, and Turin), considering different trajectory lengths to analyze error growth over a larger number of time-steps. The results prove that, although LSTM-based models are superior in unimodal scenarios, generative models perform best in those where the effects of multimodality are higher. Space-time and geographical analysis are performed, to prove the suitability of the proposed methodology for real cases and management services.Luca RossiAndrea AjmarMarina PaolantiRoberto PierdiccaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0253868 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Luca Rossi Andrea Ajmar Marina Paolanti Roberto Pierdicca Vehicle trajectory prediction and generation using LSTM models and GANs.
description	Vehicles' trajectory prediction is a topic with growing interest in recent years, as there are applications in several domains ranging from autonomous driving to traffic congestion prediction and urban planning. Predicting trajectories starting from Floating Car Data (FCD) is a complex task that comes with different challenges, namely Vehicle to Infrastructure (V2I) interaction, Vehicle to Vehicle (V2V) interaction, multimodality, and generalizability. These challenges, especially, have not been completely explored by state-of-the-art works. In particular, multimodality and generalizability have been neglected the most, and this work attempts to fill this gap by proposing and defining new datasets, metrics, and methods to help understand and predict vehicle trajectories. We propose and compare Deep Learning models based on Long Short-Term Memory and Generative Adversarial Network architectures; in particular, our GAN-3 model can be used to generate multiple predictions in multimodal scenarios. These approaches are evaluated with our newly proposed error metrics N-ADE and N-FDE, which normalize some biases in the standard Average Displacement Error (ADE) and Final Displacement Error (FDE) metrics. Experiments have been conducted using newly collected datasets in four large Italian cities (Rome, Milan, Naples, and Turin), considering different trajectory lengths to analyze error growth over a larger number of time-steps. The results prove that, although LSTM-based models are superior in unimodal scenarios, generative models perform best in those where the effects of multimodality are higher. Space-time and geographical analysis are performed, to prove the suitability of the proposed methodology for real cases and management services.
format	article
author	Luca Rossi Andrea Ajmar Marina Paolanti Roberto Pierdicca
author_facet	Luca Rossi Andrea Ajmar Marina Paolanti Roberto Pierdicca
author_sort	Luca Rossi
title	Vehicle trajectory prediction and generation using LSTM models and GANs.
title_short	Vehicle trajectory prediction and generation using LSTM models and GANs.
title_full	Vehicle trajectory prediction and generation using LSTM models and GANs.
title_fullStr	Vehicle trajectory prediction and generation using LSTM models and GANs.
title_full_unstemmed	Vehicle trajectory prediction and generation using LSTM models and GANs.
title_sort	vehicle trajectory prediction and generation using lstm models and gans.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/e61d3f56275b4caf8ed46b5374061374
work_keys_str_mv	AT lucarossi vehicletrajectorypredictionandgenerationusinglstmmodelsandgans AT andreaajmar vehicletrajectorypredictionandgenerationusinglstmmodelsandgans AT marinapaolanti vehicletrajectorypredictionandgenerationusinglstmmodelsandgans AT robertopierdicca vehicletrajectorypredictionandgenerationusinglstmmodelsandgans
_version_	1718375095999660032

Vehicle trajectory prediction and generation using LSTM models and GANs.

Ejemplares similares