A Particle-Based Collision Probability Estimation Framework for Uncertainty-Aware Risk Evaluation in Autonomous Vehicles

Risk evaluation is a critical task for the safety of autonomous driving systems. A significant factor for evaluating risk is estimating collision probability, as it indicates the rate of exposure to collision events. To obtain an accurate representation of collision probability, possible situational...

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Autores: Hossam, Abdallah, Jiménez-Bermejo, Víctor, Villagra, Jorge, Navas, Francisco, Milanés, Vicente
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2026
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:dnet:digitalcsic_::f5bbbb7a9dc21c73ec404dd92aff4665
Acceso en línea:http://hdl.handle.net/10261/428180
Access Level:acceso abierto
Palabra clave:Safety
autonomous vehicles
particle filter
collision probability
risk
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spelling A Particle-Based Collision Probability Estimation Framework for Uncertainty-Aware Risk Evaluation in Autonomous VehiclesHossam, AbdallahJiménez-Bermejo, VíctorVillagra, JorgeNavas, FranciscoMilanés, VicenteSafetyautonomous vehiclesparticle filtercollision probabilityriskRisk evaluation is a critical task for the safety of autonomous driving systems. A significant factor for evaluating risk is estimating collision probability, as it indicates the rate of exposure to collision events. To obtain an accurate representation of collision probability, possible situational variations and uncertainty in the autonomous driving system must be considered. However, most existing approaches attempt to find simplified metrics that fail to represent uncertainties effectively. In this paper, a particle-based collision probability estimator is developed. The proposed approach employs a specialized particle system to estimate collision probability while accurately capturing uncertainties in the state space of traffic participants. Its performance, evaluated against a state-of-the-art method across multiple scenarios, demonstrates its ability to effectively capture diverse collision events and represent the underlying uncertainty distribution.This work was supported in part by the European Commission through the Project SUNRISE under Grant 101069573, and in part by the Chips Joint Undertaking through Project SHAPEFUTURE under Grant 101139996.Peer reviewedInstitute of Electrical and Electronics EngineersEuropean CommissionHossam, Abdallah [0009-0008-6104-0344]Jiménez-Bermejo, Víctor [0000-0003-1197-0937]Villagra, Jorge [0000-0002-3963-7952]Milanés, Vicente [0000-0001-7096-6925]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202620262026info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/428180reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/HE/101069573info:eu-repo/grantAgreement/EC/HE/101139996https://doi.org/10.1109/access.2026.3672985Síinfo:eu-repo/semantics/openAccessoai:dnet:digitalcsic_::f5bbbb7a9dc21c73ec404dd92aff46652026-05-22T06:33:51Z
dc.title.none.fl_str_mv A Particle-Based Collision Probability Estimation Framework for Uncertainty-Aware Risk Evaluation in Autonomous Vehicles
title A Particle-Based Collision Probability Estimation Framework for Uncertainty-Aware Risk Evaluation in Autonomous Vehicles
spellingShingle A Particle-Based Collision Probability Estimation Framework for Uncertainty-Aware Risk Evaluation in Autonomous Vehicles
Hossam, Abdallah
Safety
autonomous vehicles
particle filter
collision probability
risk
title_short A Particle-Based Collision Probability Estimation Framework for Uncertainty-Aware Risk Evaluation in Autonomous Vehicles
title_full A Particle-Based Collision Probability Estimation Framework for Uncertainty-Aware Risk Evaluation in Autonomous Vehicles
title_fullStr A Particle-Based Collision Probability Estimation Framework for Uncertainty-Aware Risk Evaluation in Autonomous Vehicles
title_full_unstemmed A Particle-Based Collision Probability Estimation Framework for Uncertainty-Aware Risk Evaluation in Autonomous Vehicles
title_sort A Particle-Based Collision Probability Estimation Framework for Uncertainty-Aware Risk Evaluation in Autonomous Vehicles
dc.creator.none.fl_str_mv Hossam, Abdallah
Jiménez-Bermejo, Víctor
Villagra, Jorge
Navas, Francisco
Milanés, Vicente
author Hossam, Abdallah
author_facet Hossam, Abdallah
Jiménez-Bermejo, Víctor
Villagra, Jorge
Navas, Francisco
Milanés, Vicente
author_role author
author2 Jiménez-Bermejo, Víctor
Villagra, Jorge
Navas, Francisco
Milanés, Vicente
author2_role author
author
author
author
dc.contributor.none.fl_str_mv European Commission
Hossam, Abdallah [0009-0008-6104-0344]
Jiménez-Bermejo, Víctor [0000-0003-1197-0937]
Villagra, Jorge [0000-0002-3963-7952]
Milanés, Vicente [0000-0001-7096-6925]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Safety
autonomous vehicles
particle filter
collision probability
risk
topic Safety
autonomous vehicles
particle filter
collision probability
risk
description Risk evaluation is a critical task for the safety of autonomous driving systems. A significant factor for evaluating risk is estimating collision probability, as it indicates the rate of exposure to collision events. To obtain an accurate representation of collision probability, possible situational variations and uncertainty in the autonomous driving system must be considered. However, most existing approaches attempt to find simplified metrics that fail to represent uncertainties effectively. In this paper, a particle-based collision probability estimator is developed. The proposed approach employs a specialized particle system to estimate collision probability while accurately capturing uncertainties in the state space of traffic participants. Its performance, evaluated against a state-of-the-art method across multiple scenarios, demonstrates its ability to effectively capture diverse collision events and represent the underlying uncertainty distribution.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/428180
url http://hdl.handle.net/10261/428180
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/EC/HE/101069573
info:eu-repo/grantAgreement/EC/HE/101139996
https://doi.org/10.1109/access.2026.3672985
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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