Multi-Modal Interaction-Aware Motion Prediction at Unsignalized Intersections

Autonomous vehicle technologies have evolved quickly over the last few years, with safety being one of the key requirements for their full deployment. However, ensuring their safety while navigating through highly interactive and complex scenarios remains a critical challenge. To tackle this problem...

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Detalhes bibliográficos
Autores: Trentin, Vinicius, Artuñedo, Antonio, Godoy, Jorge, Villagrá, Jorge
Formato: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2023
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/345523
Acesso em linha:http://hdl.handle.net/10261/345523
Access Level:acceso abierto
Palavra-chave:autonomous vehicle
Motion prediction
intention-detection
interaction-aware
intersection
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repository_id_str
spelling Multi-Modal Interaction-Aware Motion Prediction at Unsignalized IntersectionsTrentin, ViniciusArtuñedo, AntonioGodoy, JorgeVillagrá, Jorgeautonomous vehicleMotion predictionintention-detectioninteraction-awareintersectionAutonomous vehicle technologies have evolved quickly over the last few years, with safety being one of the key requirements for their full deployment. However, ensuring their safety while navigating through highly interactive and complex scenarios remains a critical challenge. To tackle this problem, intention estimation and motion prediction are fundamental. In this work, a method to infer the intentions, based on a Dynamic Bayesian Network (DBN), and predict the motion, using Markov Chains, of the nearby vehicles at unsignalized intersections is proposed. This approach considers all possible corridors of the surrounding traffic participants and takes into account their interactions to infer the probabilities of stopping or crossing the intersection, as well as the probability of being in each of the possible navigable corridors. To achieve this, the DBN is used to model the relationships between the observed states and the unobserved intentions of the nearby agents. The Markov Chain model, obtained from a kinematic model, is used to predict the future motions of the vehicles, taking into account their current state, their inferred intentions, and the uncertainty associated with the prediction. The resulting multi-modal motion predictions are sent to the ego vehicle to navigate through the scene. The proposed method is evaluated in 6 real situations extracted from publicly available datasets and is compared with a model-based and a learn-based baseline models. The results showed that the proposed method outperformed both baselines in terms of accuracy considering the metrics ADE and FDE.This work has been partially funded by the Spanish Ministry of Science and Innovation with the National Project NEWCONTROL(PCI2019-103791), the Community of Madrid through SEGVAUTO 4.0-CM Programme (S2018-EMT-4362),and by the European Commission and ECSEL Joint Undertaking through the Project NEWCONTROL (826653).Peer reviewedInstitute of Electrical and Electronics EngineersMinisterio de Ciencia e Innovación (España)Comunidad 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]202420242023info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Preprintinfo:eu-repo/semantics/submittedVersionhttp://hdl.handle.net/10261/345523reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/H2020/82665310.1109/TIV.2023.3254657Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3455232026-05-22T06:33:51Z
dc.title.none.fl_str_mv Multi-Modal Interaction-Aware Motion Prediction at Unsignalized Intersections
title Multi-Modal Interaction-Aware Motion Prediction at Unsignalized Intersections
spellingShingle Multi-Modal Interaction-Aware Motion Prediction at Unsignalized Intersections
Trentin, Vinicius
autonomous vehicle
Motion prediction
intention-detection
interaction-aware
intersection
title_short Multi-Modal Interaction-Aware Motion Prediction at Unsignalized Intersections
title_full Multi-Modal Interaction-Aware Motion Prediction at Unsignalized Intersections
title_fullStr Multi-Modal Interaction-Aware Motion Prediction at Unsignalized Intersections
title_full_unstemmed Multi-Modal Interaction-Aware Motion Prediction at Unsignalized Intersections
title_sort Multi-Modal Interaction-Aware Motion Prediction at Unsignalized Intersections
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 Ministerio de Ciencia e Innovación (España)
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 autonomous vehicle
Motion prediction
intention-detection
interaction-aware
intersection
topic autonomous vehicle
Motion prediction
intention-detection
interaction-aware
intersection
description Autonomous vehicle technologies have evolved quickly over the last few years, with safety being one of the key requirements for their full deployment. However, ensuring their safety while navigating through highly interactive and complex scenarios remains a critical challenge. To tackle this problem, intention estimation and motion prediction are fundamental. In this work, a method to infer the intentions, based on a Dynamic Bayesian Network (DBN), and predict the motion, using Markov Chains, of the nearby vehicles at unsignalized intersections is proposed. This approach considers all possible corridors of the surrounding traffic participants and takes into account their interactions to infer the probabilities of stopping or crossing the intersection, as well as the probability of being in each of the possible navigable corridors. To achieve this, the DBN is used to model the relationships between the observed states and the unobserved intentions of the nearby agents. The Markov Chain model, obtained from a kinematic model, is used to predict the future motions of the vehicles, taking into account their current state, their inferred intentions, and the uncertainty associated with the prediction. The resulting multi-modal motion predictions are sent to the ego vehicle to navigate through the scene. The proposed method is evaluated in 6 real situations extracted from publicly available datasets and is compared with a model-based and a learn-based baseline models. The results showed that the proposed method outperformed both baselines in terms of accuracy considering the metrics ADE and FDE.
publishDate 2023
dc.date.none.fl_str_mv 2023
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/10261/345523
url http://hdl.handle.net/10261/345523
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
10.1109/TIV.2023.3254657
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
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 15,198674

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