Combination and Selection of Traffic Safety Expert Judgments for the Prevention of Driving Risks
dc.contributor.author | Cabello, Enrique | |
dc.contributor.author | Conde, Cristina | |
dc.contributor.author | de Diego, Isaac Martín | |
dc.contributor.author | Moguerza, Javier M. | |
dc.contributor.author | Redchuk, Andrés | |
dc.date.accessioned | 2019-10-22T15:59:26Z | |
dc.date.available | 2019-10-22T15:59:26Z | |
dc.date.issued | 2012-11 | |
dc.identifier.other | DOI 10.3390/s121114711 | |
dc.identifier.uri | https://repositorio.unlz.edu.ar/handle/123456789/126 | |
dc.description.abstract | In 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.iso | en | es |
dc.subject | driving risks | es |
dc.subject | fusion of judgments | es |
dc.subject | selection of experts | es |
dc.subject | regression | es |
dc.title | Combination and Selection of Traffic Safety Expert Judgments for the Prevention of Driving Risks | es |
dc.type | Article | es |
dcterms.license | Attribution 4.0 International (BY 4.0) | es |