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dc.contributor.authorGorospe, Joseba
dc.contributor.authorAlonso Gómez, Arrate
dc.contributor.otherHasan, Shahriar
dc.contributor.otherIslam, Mir Riyanul
dc.contributor.otherGirs, Svetlana
dc.contributor.otherUhlemann, Elisabeth
dc.date.accessioned2024-02-19T14:44:18Z
dc.date.available2024-02-19T14:44:18Z
dc.date.issued2023
dc.identifier.isbn979-8-3503-3667-2en
dc.identifier.issn2160-4886en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=173332en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6260
dc.description.abstractAutomated Vehicles (AVs) require sensing and perception to integrate data from multiple sources, such as cameras, lidars, and radars, to operate safely and efficiently. Collaborative sensing through wireless vehicular communications can enhance this process. However, failures in sensors and communication systems may require the vehicle to perform a safe stop or emergency braking when encountering hazards. By identifying the conditions for being able to perform emergency braking without collisions, better automation models that also consider communications need to be developed. Hence, we propose to employ Machine Learning (ML) to predict inter-vehicle collisions during emergency braking by utilizing a comprehensive dataset that has been prepared through rigorous simulations. Using simulations and data-driven modeling has several advantages over physics-based models in this case, as it, e.g., enables us to provide a dataset with varying vehicle kinematic parameters, traffic density, network load, vehicle automation controller parameters, and more. To further establish the conditions for inter-vehicle collisions, we analyze the predictions made through interpretable ML models and rank the features that contribute to collisions. We also extract human-interpretable rules that can establish the conditions leading to collisions between AVs during emergency braking. Finally, we plot the decision boundaries between different input features to separate the collision and non-collision classes and demonstrate the safe region of emergency braking.en
dc.language.isoengen
dc.publisherIEEEen
dc.rights© 2023 IEEEen
dc.subjectWireless communicationen
dc.subjectanalytical modelingen
dc.subjectWireless sensor networksen
dc.subjectAutomationen
dc.subjectMachine learningen
dc.subjectPredictive modelen
dc.subjectSensor systemsen
dc.subjectODS 9 Industria, innovación e infraestructuraes
dc.titleAnalyzing Inter-Vehicle Collision Predictions during Emergency Braking with Automated Vehiclesen
dcterms.accessRightshttp://purl.org/coar/access_right/c_f1cfen
dcterms.sourceInternational Conference on Wireless and Mobile Computing, Networking and Communicationsen
local.contributor.groupTeoría de la señal y comunicacioneses
local.description.peerreviewedtrueen
local.description.publicationfirstpage411en
local.description.publicationlastpage418en
local.identifier.doihttps://doi.org/10.1109/WiMob58348.2023.10187826en
local.embargo.enddate2025-06-30
local.contributor.otherinstitutionMälardalen Universityes
local.source.details2023. Vol. June. Pp. 411-418en
oaire.format.mimetypeapplication/pdfen
oaire.file$DSPACE\assetstoreen
oaire.resourceTypehttp://purl.org/coar/resource_type/c_c94fen
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaen
oaire.funderNameEusko Jaurlaritza = Gobierno Vasco
oaire.funderNameEusko Jaurlaritza = Gobierno Vasco
oaire.funderNameEuropean Commission
oaire.funderNameEuropean Commission
oaire.funderIdentifierhttps://ror.org/00pz2fp31 http://data.crossref.org/fundingdata/funder/10.13039/501100003086
oaire.funderIdentifierhttps://ror.org/00pz2fp31 http://data.crossref.org/fundingdata/funder/10.13039/501100003086
oaire.funderIdentifierhttps://ror.org/00k4n6c32 http://data.crossref.org/fundingdata/funder/10.13039/501100000780
oaire.funderIdentifierhttps://ror.org/00k4n6c32 http://data.crossref.org/fundingdata/funder/10.13039/501100000780
oaire.fundingStreamIkertalde Convocatoria 2022-2023
oaire.fundingStreamElkartek 2021
oaire.fundingStreamH2020
oaire.fundingStreamH2020-ECSEL
oaire.awardNumberIT1451-22
oaire.awardNumberKK-2021-00123
oaire.awardNumber764951
oaire.awardNumber101007350
oaire.awardTitleTeoría de la Señal y Comunicaciones
oaire.awardTitleEvolución tecnológica para la automatización multivehicular y evaluación de funciones de conducción altamente automatizadas (AUTOEV@L)
oaire.awardTitleImmersive Visual Technologies for Safety-critical Applications (ImmerSAFE)
oaire.awardTitleAI-augmented automation for efficient DevOps, a model-based framework for continuous development At RunTime in cyber-physical systems (AIDOaRt)
oaire.awardURISin información
oaire.awardURISin información
oaire.awardURIhttps://doi.org/10.3030/764951
oaire.awardURIhttps://doi.org/10.3030/101007350


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