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dc.contributor.authorCabello, Enrique
dc.contributor.authorConde, Cristina
dc.contributor.authorde Diego, Isaac Martín
dc.contributor.authorMoguerza, Javier M.
dc.contributor.authorRedchuk, Andrés
dc.date.accessioned2019-10-22T15:59:26Z
dc.date.available2019-10-22T15:59:26Z
dc.date.issued2012-11
dc.identifier.otherDOI 10.3390/s121114711
dc.identifier.urihttps://repositorio.unlz.edu.ar/handle/123456789/126
dc.description.abstractIn this paper, we describe a new framework to combine experts’ judgments forthe prevention of driving risks in a cabin truck. In addition, the methodology shows how tochoose among the experts the one whose predictions fit best the environmental conditions.The methodology is applied over data sets obtained from a high immersive cabin trucksimulator in natural driving conditions. A nonparametric model, based in NearestNeighbors combined with Restricted Least Squared methods is developed. Three expertswere asked to evaluate the driving risk using a Visual Analog Scale (VAS), in order tomeasure the driving risk in a truck simulator where the vehicle dynamics factors werestored. Numerical results show that the methodology is suitable for embedding in real timesystems.es
dc.language.isoenes
dc.subjectdriving riskses
dc.subjectfusion of judgmentses
dc.subjectselection of expertses
dc.subjectregressiones
dc.titleCombination and Selection of Traffic Safety Expert Judgments for the Prevention of Driving Riskses
dc.typeArticlees
dcterms.licenseAttribution 4.0 International (BY 4.0)es


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