A Comparison of Lateral Intention Models for Interaction-aware Motion Prediction at Highways

To safely navigate in complex scenarios is crucial to know the predictions of the vehicles involved in the scene. The future behavior of the traffic participants is dependent on their intentions, the road layout and the interaction between them. In this work, a framework is presented to compute the...

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Detalles Bibliográficos
Autores: Trentin Vinicius, Artuñedo, Antonio, Godoy, Jorge, Villagrá, Jorge
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2021
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/351987
Acceso en línea:http://hdl.handle.net/10261/351987
Access Level:acceso abierto
Palabra clave:Interaction-aware
motion prediction
Lane Change
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spelling A Comparison of Lateral Intention Models for Interaction-aware Motion Prediction at HighwaysTrentin ViniciusArtuñedo, AntonioGodoy, JorgeVillagrá, JorgeInteraction-awaremotion predictionLane ChangeTo safely navigate in complex scenarios is crucial to know the predictions of the vehicles involved in the scene. The future behavior of the traffic participants is dependent on their intentions, the road layout and the interaction between them. In this work, a framework is presented to compute the motion predictions of the surrounding vehicles considering all possible routes obtained from a given map. At each time step, with a Dynamic Bayesian Network, the probability of being on a specific route and the intention to change lanes are computed. Our framework, based on Markov chains, is generic and can handle various road layouts and any number of vehicles. We apply the framework in a two-lane highway and evaluate the influence of different lane-changing methods on the predictions of the vehicles present at the scene.This work has been partially funded by the Spanish Ministry of Science and Innovation, the Community of Madrid through SEGVAUTO 4.0-CM (S2018-EMT-4362) Programme, and by the European Commission and ECSEL Joint Undertaking through the Projects NEWCONTROL (826653) and SECREDAS(783119).Peer reviewedScience and technology publicationComunidad de MadridEuropean CommissionTrentin Vinicius [0000-0001-5732-3263]Artuñedo, Antonio [0000-0003-2161-9876]Godoy, Jorge [0000-0002-3132-5348]Villagrá, Jorge [0000-0002-3963-7952]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242021info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Preprintinfo:eu-repo/semantics/submittedVersionhttp://hdl.handle.net/10261/351987reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/H2020/826653DOI: 10.5220/0010460701800191Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3519872026-05-22T06:33:51Z
dc.title.none.fl_str_mv A Comparison of Lateral Intention Models for Interaction-aware Motion Prediction at Highways
title A Comparison of Lateral Intention Models for Interaction-aware Motion Prediction at Highways
spellingShingle A Comparison of Lateral Intention Models for Interaction-aware Motion Prediction at Highways
Trentin Vinicius
Interaction-aware
motion prediction
Lane Change
title_short A Comparison of Lateral Intention Models for Interaction-aware Motion Prediction at Highways
title_full A Comparison of Lateral Intention Models for Interaction-aware Motion Prediction at Highways
title_fullStr A Comparison of Lateral Intention Models for Interaction-aware Motion Prediction at Highways
title_full_unstemmed A Comparison of Lateral Intention Models for Interaction-aware Motion Prediction at Highways
title_sort A Comparison of Lateral Intention Models for Interaction-aware Motion Prediction at Highways
dc.creator.none.fl_str_mv Trentin Vinicius
Artuñedo, Antonio
Godoy, Jorge
Villagrá, Jorge
author Trentin Vinicius
author_facet Trentin Vinicius
Artuñedo, Antonio
Godoy, Jorge
Villagrá, Jorge
author_role author
author2 Artuñedo, Antonio
Godoy, Jorge
Villagrá, Jorge
author2_role author
author
author
dc.contributor.none.fl_str_mv Comunidad de Madrid
European Commission
Trentin Vinicius [0000-0001-5732-3263]
Artuñedo, Antonio [0000-0003-2161-9876]
Godoy, Jorge [0000-0002-3132-5348]
Villagrá, Jorge [0000-0002-3963-7952]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Interaction-aware
motion prediction
Lane Change
topic Interaction-aware
motion prediction
Lane Change
description To safely navigate in complex scenarios is crucial to know the predictions of the vehicles involved in the scene. The future behavior of the traffic participants is dependent on their intentions, the road layout and the interaction between them. In this work, a framework is presented to compute the motion predictions of the surrounding vehicles considering all possible routes obtained from a given map. At each time step, with a Dynamic Bayesian Network, the probability of being on a specific route and the intention to change lanes are computed. Our framework, based on Markov chains, is generic and can handle various road layouts and any number of vehicles. We apply the framework in a two-lane highway and evaluate the influence of different lane-changing methods on the predictions of the vehicles present at the scene.
publishDate 2021
dc.date.none.fl_str_mv 2021
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Preprint
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/351987
url http://hdl.handle.net/10261/351987
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/EC/H2020/826653
DOI: 10.5220/0010460701800191
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Science and technology publication
publisher.none.fl_str_mv Science and technology publication
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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