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dc.contributor.authorToledo Moreo, Rafael 
dc.date.accessioned2008-09-15T10:41:30Z
dc.date.available2008-09-15T10:41:30Z
dc.date.issued2007
dc.identifier.citationTOLEDO MOREO, R., PINZOLAS PRADO, J., CANO IZQUIERDO, J.M. Supervised dFasArt: a Neuro-Fuzzy Dynamic Architecture for Maneuver Detection in Road Vehicle Collision Avoidance Support System. Lecture Notes in Computer Sciences: 419-428, 2007es
dc.identifier.issn0302-9743
dc.description.abstractThe issue of collision avoidance in road vehicles has been investigated from many different points of view. An interesting approach for Road Vehicle Collision Assistance Support Systems (RVCASS) is based on the creation of a scene of the vehicles involved in a potentially conflictive traffic situation. This paper proposes a neuro-fuzzy approach for dynamic classification of the vehicles roles in a scene. For that purpose, different maneuver state models for longitudinal movements of road vehicles have been defined, and a prototype has been equipped with INS (Inertial Navigation Systems) and GPS (Global Positioning System) sensors. Trials with real data show the suitability of the proposed neurofuzzy approach for solving support to the problem under consideration.es
dc.formatapplication/pdf
dc.language.isoenges
dc.publisherSpringer-verlages
dc.rightsPublicación original disponible en www.springerlink.com
dc.rightsLicencia Creative Commons
dc.titleSupervised dFasArt: A Neuro-Fuzzy Dynamic Architecture of Maneuver Detection in Road Vehicle Collision Avoidance Support Systems.es
dc.typeinfo:eu-repo/semantics/articlees
dc.subjectdFasArtes
dc.subjectCollision Avoidancees
dc.subject.otherTecnología Electrónicaes
dc.identifier.urihttp://hdl.handle.net/10317/426
dc.identifier.doi10.1007/978-3-540-73055-2_44
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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