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Combination and Selection of Traffic Safety Expert Judgments for the Prevention of Driving Risks
Department of Computer Architecture, University Rey Juan Carlos, Móstoles 28933, Spain
Department of Statistics and Operations Research, University Rey Juan Carlos, Fuenlabrada 28943, Spain
Current address: Facultad de Ciencias Empresariales, Universidad Autónoma de Chile, Providencia, Santiago, Chile.
* Author to whom correspondence should be addressed.
Received: 5 September 2012; in revised form: 14 October 2012 / Accepted: 29 October 2012 / Published: 2 November 2012
Abstract: In this paper, we describe a new framework to combine experts’ 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.
Keywords: driving risks; fusion of judgments; selection of experts; regression
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Cabello, E.; Conde, C.; Diego, I.M.; Moguerza, J.M.; Redchuk, A. Combination and Selection of Traffic Safety Expert Judgments for the Prevention of Driving Risks. Sensors 2012, 12, 14711-14729.
Cabello E, Conde C, Diego IM, Moguerza JM, Redchuk A. Combination and Selection of Traffic Safety Expert Judgments for the Prevention of Driving Risks. Sensors. 2012; 12(11):14711-14729.
Cabello, Enrique; Conde, Cristina; Diego, Isaac M.; Moguerza, Javier M.; Redchuk, Andrés. 2012. "Combination and Selection of Traffic Safety Expert Judgments for the Prevention of Driving Risks." Sensors 12, no. 11: 14711-14729.