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dc.contributor.authorMatute Peaspan, Jose A.
dc.contributor.authorMarcano Sandoval, Mauricio
dc.contributor.authorDíaz, Sergio
dc.contributor.authorZubizarreta Pico, Asier
dc.contributor.authorPérez Rastelli, Joshue Manuel
dc.date.accessioned2020-10-27T11:18:50Z
dc.date.available2020-10-27T11:18:50Z
dc.date.issued2020-10-13
dc.identifier.citationElectronics 9(10) : (2020) // Article ID 1674es_ES
dc.identifier.issn2079-9292
dc.identifier.urihttp://hdl.handle.net/10810/47304
dc.description.abstractsettings Open AccessArticle Lateral-Acceleration-Based Vehicle-Models-Blending for Automated Driving Controllers by Jose A. Matute-Peaspan 1,2,* [OrcID] , Mauricio Marcano 1,2 [OrcID] , Sergio Diaz 1 [OrcID] , Asier Zubizarreta 2 [OrcID] and Joshue Perez 1 [OrcID] 1 TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain 2 Department of Automatic Control and Systems Engineering, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain * Author to whom correspondence should be addressed. Electronics 2020, 9(10), 1674; https://doi.org/10.3390/electronics9101674 Received: 4 September 2020 / Revised: 26 September 2020 / Accepted: 8 October 2020 / Published: 13 October 2020 (This article belongs to the Special Issue Autonomous Vehicles Technology) Download PDF Browse Figures Review Reports Abstract Model-based trajectory tracking has become a widely used technique for automated driving system applications. A critical design decision is the proper selection of a vehicle model that achieves the best trade-off between real-time capability and robustness. Blending different types of vehicle models is a recent practice to increase the operating range of model-based trajectory tracking control applications. However, current approaches focus on the use of longitudinal speed as the blending parameter, with a formal procedure to tune and select its parameters still lacking. This work presents a novel approach based on lateral accelerations, along with a formal procedure and criteria to tune and select blending parameters, for its use on model-based predictive controllers for autonomous driving. An electric passenger bus traveling at different speeds over urban routes is proposed as a case study. Results demonstrate that the lateral acceleration, which is proportional to the lateral forces that differentiate kinematic and dynamic models, is a more appropriate model-switching enabler than the currently used longitudinal velocity. Moreover, the advanced procedure to define blending parameters is shown to be effective. Finally, a smooth blending method offers better tracking results versus sudden model switching ones and non-blending techniques.es_ES
dc.description.sponsorshipThis research was funded by AUTODRIVE within the Electronic Components and Systems for European Leadership Joint Undertaking (ECSEL JU) in collaboration with the European Union’s H2020 Framework Program (H2020/2014-2020) and National Authorities, under Grant No. 737469.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/737469.es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subjectvehicle-model blendinges_ES
dc.subjecttrajectory trackinges_ES
dc.subjectmodel predictive controles_ES
dc.subjectautomated drivinges_ES
dc.subjectvehicle controles_ES
dc.titleLateral-Acceleration-Based Vehicle-Models-Blending for Automated Driving Controllerses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.date.updated2020-10-26T14:22:20Z
dc.rights.holder2020 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/).es_ES
dc.relation.publisherversionhttps://www.mdpi.com/2079-9292/9/10/1674/htmes_ES
dc.identifier.doi10.3390/electronics9101674
dc.contributor.funderEuropean Commission
dc.departamentoesIngeniería de sistemas y automática
dc.departamentoeuSistemen ingeniaritza eta automatika


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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/).
Except where otherwise noted, this item's license is described as 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/).