A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the street is a key task in many areas, e.g., self-drivin...

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Autores principales:	Bogdan Ilie Sighencea, Rareș Ion Stanciu, Cătălin Daniel Căleanu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	trajectory prediction pedestrian behavior autonomous vehicles sensor technologies deep learning Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/23f0e81008644e838af699bf1b5c8647
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id	oai:doaj.org-article:23f0e81008644e838af699bf1b5c8647
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spelling	oai:doaj.org-article:23f0e81008644e838af699bf1b5c86472021-11-25T18:57:20ZA Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction10.3390/s212275431424-8220https://doaj.org/article/23f0e81008644e838af699bf1b5c86472021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7543https://doaj.org/toc/1424-8220Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the street is a key task in many areas, e.g., self-driving auto vehicles, mobile robots or advanced surveillance systems, and they still represent a technological challenge. The performance of state-of-the-art pedestrian trajectory prediction methods currently benefits from the advancements in sensors and associated signal processing technologies. The current paper reviews the most recent deep learning-based solutions for the problem of pedestrian trajectory prediction along with employed sensors and afferent processing methodologies, and it performs an overview of the available datasets, performance metrics used in the evaluation process, and practical applications. Finally, the current work exposes the research gaps from the literature and outlines potential new research directions.Bogdan Ilie SighenceaRareș Ion StanciuCătălin Daniel CăleanuMDPI AGarticletrajectory predictionpedestrian behaviorautonomous vehiclessensor technologiesdeep learningChemical technologyTP1-1185ENSensors, Vol 21, Iss 7543, p 7543 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	trajectory prediction pedestrian behavior autonomous vehicles sensor technologies deep learning Chemical technology TP1-1185
spellingShingle	trajectory prediction pedestrian behavior autonomous vehicles sensor technologies deep learning Chemical technology TP1-1185 Bogdan Ilie Sighencea Rareș Ion Stanciu Cătălin Daniel Căleanu A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction
description	Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the street is a key task in many areas, e.g., self-driving auto vehicles, mobile robots or advanced surveillance systems, and they still represent a technological challenge. The performance of state-of-the-art pedestrian trajectory prediction methods currently benefits from the advancements in sensors and associated signal processing technologies. The current paper reviews the most recent deep learning-based solutions for the problem of pedestrian trajectory prediction along with employed sensors and afferent processing methodologies, and it performs an overview of the available datasets, performance metrics used in the evaluation process, and practical applications. Finally, the current work exposes the research gaps from the literature and outlines potential new research directions.
format	article
author	Bogdan Ilie Sighencea Rareș Ion Stanciu Cătălin Daniel Căleanu
author_facet	Bogdan Ilie Sighencea Rareș Ion Stanciu Cătălin Daniel Căleanu
author_sort	Bogdan Ilie Sighencea
title	A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction
title_short	A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction
title_full	A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction
title_fullStr	A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction
title_full_unstemmed	A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction
title_sort	review of deep learning-based methods for pedestrian trajectory prediction
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/23f0e81008644e838af699bf1b5c8647
work_keys_str_mv	AT bogdaniliesighencea areviewofdeeplearningbasedmethodsforpedestriantrajectoryprediction AT raresionstanciu areviewofdeeplearningbasedmethodsforpedestriantrajectoryprediction AT catalindanielcaleanu areviewofdeeplearningbasedmethodsforpedestriantrajectoryprediction AT bogdaniliesighencea reviewofdeeplearningbasedmethodsforpedestriantrajectoryprediction AT raresionstanciu reviewofdeeplearningbasedmethodsforpedestriantrajectoryprediction AT catalindanielcaleanu reviewofdeeplearningbasedmethodsforpedestriantrajectoryprediction
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A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

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