An eco-driving approach for ride comfort improvement
dc.contributor.author | Mata Carballeira, Oscar | |
dc.contributor.author | Del Campo Hagelstrom, Inés Juliana | |
dc.contributor.author | Asua Uriarte, Estibaliz | |
dc.date.accessioned | 2022-02-11T08:41:52Z | |
dc.date.available | 2022-02-11T08:41:52Z | |
dc.date.issued | 2022-02 | |
dc.identifier.citation | IET Intelligent Transport Systems 16(2) : 186-205 (2022) | es_ES |
dc.identifier.issn | 1751-956X | |
dc.identifier.issn | 1751-9578 | |
dc.identifier.uri | http://hdl.handle.net/10810/55429 | |
dc.description.abstract | [EN] New challenges on transport systems are emerging due to the advances that the current paradigm is experiencing. The breakthrough of the autonomous car brings concerns about ride comfort, while the pollution concerns have arisen in recent years. In the model of automated automobiles, drivers are expected to become passengers, so, they will be more prone to suffer from ride discomfort or motion sickness. Conversely, the eco-driving implications should not be set aside because of the influence of pollution on climate and people's health. For that reason, a joint assessment of the aforementioned points would have a positive impact. Thus, this work presents a self-organised map-based solution to assess ride comfort features of individuals considering their driving style from the viewpoint of eco-driving. For this purpose, a previously acquired dataset from an instrumented car was used to classify drivers regarding the causes of their lack of ride comfort and eco-friendliness. Once drivers are classified regarding their driving style, natural-language-based recommendations are proposed to increase the engagement with the system. Hence, potential improvements of up to the 57.7% for ride comfort evaluation parameters, as well as up to the 47.1% in greenhouse-gasses emissions are expected to be reached. | es_ES |
dc.description.sponsorship | University of the Basque Country UPV/EHU, Grant/Award Number: GIU18/122; European Commission, Grant/Award Number: TEC201677618-R; Spanish AEI, Grant/Award Number: TEC2016-77618-R; Basque Government, Grant/Award Number: KK-2019-00035-AUTOLIB (ELKARTEK) | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Wiley | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/TEC2016-77618-R | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | fuel consumption | es_ES |
dc.subject | motion sickness | es_ES |
dc.subject | impact | es_ES |
dc.subject | driver | es_ES |
dc.subject | frequency | es_ES |
dc.subject | emissions | es_ES |
dc.subject | behavior | es_ES |
dc.subject | style | es_ES |
dc.title | An eco-driving approach for ride comfort improvement | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2021 The Authors.IET Intelligent Transport Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work isproperly cited. | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/itr2.12137 | es_ES |
dc.identifier.doi | 10.1049/itr2.12137 | |
dc.departamentoes | Electricidad y electrónica | es_ES |
dc.departamentoeu | Elektrizitatea eta elektronika | es_ES |
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Except where otherwise noted, this item's license is described as © 2021 The Authors.IET Intelligent Transport Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work isproperly cited.