title: Combination and selection of traffic safety expert judgments for the prevention of driving risks.
abstract: In this paper, we describe a new framework to combine experts&#8217; judgments for the prevention of driving risks in a cabin truck. In addition, the methodology shows how to choose 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 truck simulator in natural driving conditions. A nonparametric model, based in Nearest Neighbors combined with Restricted Least Squared methods is developed. Three experts were asked to evaluate the driving risk using a Visual Analog Scale (VAS), in order to measure the driving risk in a truck simulator where the vehicle dynamics factors were stored. Numerical results show that the methodology is suitable for embedding in real  time systems.
authors.affiliation: Department of Computer Architecture, University Rey Juan Carlos, Móstoles 28933, Spain. enrique.cabello@urjc.es
authors.forename: Enrique
authors.initials: E
authors.lastname: Cabello
authors.forename: Cristina
authors.initials: C
authors.lastname: Conde
authors.forename: Isaac Martín
authors.initials: IM
authors.lastname: de Diego
authors.forename: Javier M
authors.initials: JM
authors.lastname: Moguerza
authors.forename: Andrés
authors.initials: A
authors.lastname: Redchuk
mesh_terms: D001334:Automobile Driving; D006801:Humans; D008962:Models, Theoretical; D018570:Risk Assessment; D040242:Risk Reduction Behavior; D012449:Safety