Show simple item record

dc.contributor.authorDel Campo Hagelstrom, Inés Juliana ORCID
dc.contributor.authorMartínez González, María Victoria
dc.contributor.authorEchanove Arias, Francisco Javier ORCID
dc.contributor.authorAsua Uriarte, Estibaliz ORCID
dc.contributor.authorFinker de la Iglesia, Raúl ORCID
dc.contributor.authorBasterrechea Oyarzabal, Koldobika
dc.date.accessioned2024-02-08T11:11:12Z
dc.date.available2024-02-08T11:11:12Z
dc.date.issued2019-08-05
dc.identifier.citationNeural Computing and Applications 31(12) : 8871-8886 (2019)es_ES
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.urihttp://hdl.handle.net/10810/65460
dc.description.abstractIn the present scenario of technological breakthroughs in the automotive industry, machine learning is greatly contributing to the development of safer and more comfortable vehicles. In particular, personalization of the driving experience using machine learning is an innovative trend that comprises the development of both customized driver assistance systems and in-cabin comfort features. In this work, a versatile hardware/software platform for personalized driver assistance, using online sequential extreme learning machines (OS-ELM), is presented. The system, based on a programmable system-on-chip (SoC), is able to recognize the driver and personalize the behavior of the car. The platform provides high speed, small size, efficient power consumption, and true capability for real-time adaptation (i.e., on-chip self-learning). In addition, due to the plasticity and scalability of the OS-ELM algorithm and the programmable nature of the SoC, this solution is flexible enough to cope with the incremental changes that the new generation of vehicles are demanding. The implementation details of a system, suitable for current levels of driving automation, are provided.es_ES
dc.description.sponsorshipThis work was supported in part by the Spanish Ministry of Economy and Competitiveness (MINECO) under Grant TEC2013-42286-R and by the Basque Country University UPV/EHU under Grant PPG17/20.es_ES
dc.description.sponsorshiphis work was supported in part by the Spanish Ministry of Economy and Competitiveness (MINECO) under Grant TEC2013-42286-R and by the Basque Country University UPV/EHU under Grant PPG17/20.
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/TEC2013-42286-R
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectdriver assistance systems (DASs)es_ES
dc.subjectextreme learning machine
dc.subjectonline learning
dc.subjectmulti-objective optimization
dc.subjectfield-programmable gate arrays (FPGA)
dc.subjectsystem-on-chip (SoC)
dc.titleA versatile hardware/software platform for personalized driver assistance based on online sequential extreme learning machineses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder© 2019, Springer-Verlag London Ltd., part of Springer Nature*
dc.relation.publisherversionhttps://link.springer.com/article/10.1007/s00521-019-04386-4
dc.identifier.doi10.1007/s00521-019-04386-4
dc.departamentoesElectricidad y electrónicaes_ES
dc.departamentoeuElektrizitatea eta elektronikaes_ES


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record