Resumen:
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.
Resumen:
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.
