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dc.contributor.author | Salazar Afanador, Addisson | es_ES |
dc.contributor.author | Rodríguez, Alberto | es_ES |
dc.contributor.author | Vargas, Nancy | es_ES |
dc.contributor.author | Vergara Domínguez, Luís | es_ES |
dc.date.accessioned | 2023-10-24T18:01:21Z | |
dc.date.available | 2023-10-24T18:01:21Z | |
dc.date.issued | 2022-04 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/198747 | |
dc.description.abstract | [EN] It is demonstrated that data augmentation is a promising approach to reduce the size of the captured dataset required for training automatic road surface classifiers. The context is on-board systems for autonomous or semi-autonomous driving assistance: automatic power-assisted steering. Evidence is obtained by extensive experiments involving multiple captures from a 10-channel multisensor deployment: three channels from the accelerometer (acceleration in the X, Y, and Z axes); three microphone channels; two speed channels; and the torque and position of the handwheel. These captures were made under different settings: three worm-gear interface configurations; hands on or off the wheel; vehicle speed (constant speed of 10, 15,20, 30 km/h, or accelerating from 0 to 30 km/h); and road surface (smooth flat asphalt, stripes, or cobblestones). It has been demonstrated in the experiments that data augmentation allows a reduction by an approximate factor of 1.5 in the size of the captured training dataset. | es_ES |
dc.description.sponsorship | This research was funded by MCIN/AEI/10.13039/501100011033 and by the European Union, grant number TEC2017-84743-P. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.ispartof | Applied Sciences | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Driving assistance | es_ES |
dc.subject | Road surface classification | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | Data augmentation | es_ES |
dc.subject.classification | TEORÍA DE LA SEÑAL Y COMUNICACIONES | es_ES |
dc.title | On Training Road Surface Classifiers by Data Augmentation | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/app12073423 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TEC2017-84743-P/ES/METODOS INFORMADOS PARA LA SINTESIS DE SEÑALES/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Telecomunicación - Escola Tècnica Superior d'Enginyers de Telecomunicació | es_ES |
dc.description.bibliographicCitation | Salazar Afanador, A.; Rodríguez, A.; Vargas, N.; Vergara Domínguez, L. (2022). On Training Road Surface Classifiers by Data Augmentation. Applied Sciences. 12(7):1-11. https://doi.org/10.3390/app12073423 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/app12073423 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 11 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 12 | es_ES |
dc.description.issue | 7 | es_ES |
dc.identifier.eissn | 2076-3417 | es_ES |
dc.relation.pasarela | S\495459 | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
dc.contributor.funder | Universitat Politècnica de València | es_ES |