Combination and Selection of Traffic Safety Expert Judgments for the Prevention of Driving Risks
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Data
2012-11Autor
Cabello, Enrique
Conde, Cristina
de Diego, Isaac Martín
Moguerza, Javier M.
Redchuk, Andrés
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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.