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dc.contributor.authorDel Campo Hagelstrom, Inés Juliana ORCID
dc.contributor.authorAsua Uriarte, Estibaliz ORCID
dc.contributor.authorMartínez González, María Victoria
dc.contributor.authorMata Carballeira, Oscar ORCID
dc.contributor.authorEchanove Arias, Francisco Javier ORCID
dc.date.accessioned2025-01-31T22:23:33Z
dc.date.available2025-01-31T22:23:33Z
dc.date.issued2018-12-09
dc.identifier.citation21st International Conference on Intelligent Transportation Systems (ITSC) : 3251-3258 (2018)es_ES
dc.identifier.isbn978-1-7281-0323-5
dc.identifier.issn2153-0017
dc.identifier.urihttp://hdl.handle.net/10810/72147
dc.description.abstractDriving style (DS) classification and identification plays an increasingly important role in the development of advanced driver assistance systems and automated vehicles. Both the enhancement of driving safety and the improvement of fuel efficiency are essential goals of current research in driving style characterization. However, the comfort perspective has still hardly been investigated, despite its importance for the future of driving automation. This paper proposes a driving style classification method, focused on global comfort of the driver and the passengers, but which can also be integrated into the above safety-efficiency viewpoint. Although human comfort in vehicles is affected by different factors, the amplitude and frequency of accelerations are recognized as key signals for assessing driving comfort. The proposed DS classification approach is based on a hybrid machine learning method that combines an unsupervised clustering method with a data-driven extreme learning machine (ELM) algorithm. Hierarchical clustering is used to explore the relevance of the acceleration components in relation to ride comfort, while a single layer ELM topology is implemented to model the DS classifier. The method has been evaluated using experimental data obtained with an instrumented car equipped with in-vehicle sensors and measurement units. The obtained clustering results are consistent with comfort standard indicators, while the data-driven algorithm provides encouraging results: more than 95 % classification rate using unseen data.es_ES
dc.language.isoenges_ES
dc.publisherIEEEes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.titleDriving Style Recognition based on Ride Comfort Using a Hybrid Machine Learning Algorithmes_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.holder© 2018 IEEEes_ES
dc.relation.publisherversionhttps://doi.org/10.1109/ITSC.2018.8569722es_ES
dc.identifier.doi10.1109/ITSC.2018.8569722
dc.departamentoesElectricidad y electrónicaes_ES
dc.departamentoeuElektrizitatea eta elektronikaes_ES


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