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Dataset Construction from Naturalistic Driving in Roundabouts

A proper driver characterization in complex environments using computational techniques depends on the richness and variety of data obtained from naturalistic driving. The present article proposes the construction of a dataset from naturalistic driving specific to maneuvers in roundabouts and makes...

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Autores principales: García Cuenca, Laura, Guindel, Carlos, Aliane, Nourdine, Armingol, José María, Fernández Andrés, Javier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7764875/
https://www.ncbi.nlm.nih.gov/pubmed/33322242
http://dx.doi.org/10.3390/s20247151
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author García Cuenca, Laura
Guindel, Carlos
Aliane, Nourdine
Armingol, José María
Fernández Andrés, Javier
author_facet García Cuenca, Laura
Guindel, Carlos
Aliane, Nourdine
Armingol, José María
Fernández Andrés, Javier
author_sort García Cuenca, Laura
collection PubMed
description A proper driver characterization in complex environments using computational techniques depends on the richness and variety of data obtained from naturalistic driving. The present article proposes the construction of a dataset from naturalistic driving specific to maneuvers in roundabouts and makes it open and available to the scientific community for performing their own studies. The dataset is a combination of data gathered from on-board instrumentation and data obtained from the post-processing of maps as well as recorded videos. The approach proposed in this paper consists of handling roundabouts as a stretch of road that includes 100 m before the entrance, the internal part, and 100 m after the exit. This stretch of road is then spatially sampled in small sections to which data are associated.
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spelling pubmed-77648752020-12-27 Dataset Construction from Naturalistic Driving in Roundabouts García Cuenca, Laura Guindel, Carlos Aliane, Nourdine Armingol, José María Fernández Andrés, Javier Sensors (Basel) Article A proper driver characterization in complex environments using computational techniques depends on the richness and variety of data obtained from naturalistic driving. The present article proposes the construction of a dataset from naturalistic driving specific to maneuvers in roundabouts and makes it open and available to the scientific community for performing their own studies. The dataset is a combination of data gathered from on-board instrumentation and data obtained from the post-processing of maps as well as recorded videos. The approach proposed in this paper consists of handling roundabouts as a stretch of road that includes 100 m before the entrance, the internal part, and 100 m after the exit. This stretch of road is then spatially sampled in small sections to which data are associated. MDPI 2020-12-13 /pmc/articles/PMC7764875/ /pubmed/33322242 http://dx.doi.org/10.3390/s20247151 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
García Cuenca, Laura
Guindel, Carlos
Aliane, Nourdine
Armingol, José María
Fernández Andrés, Javier
Dataset Construction from Naturalistic Driving in Roundabouts
title Dataset Construction from Naturalistic Driving in Roundabouts
title_full Dataset Construction from Naturalistic Driving in Roundabouts
title_fullStr Dataset Construction from Naturalistic Driving in Roundabouts
title_full_unstemmed Dataset Construction from Naturalistic Driving in Roundabouts
title_short Dataset Construction from Naturalistic Driving in Roundabouts
title_sort dataset construction from naturalistic driving in roundabouts
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7764875/
https://www.ncbi.nlm.nih.gov/pubmed/33322242
http://dx.doi.org/10.3390/s20247151
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