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Combination and Selection of Traffic Safety Expert Judgments for the Prevention of Driving Risks

Author(s): Enrique Cabello | Cristina Conde | Isaac Martín de Diego | Javier M. Moguerza | Andrés Redchuk

Journal: Sensors
ISSN 1424-8220

Volume: 12;
Issue: 11;
Start page: 14711;
Date: 2012;
Original page

Keywords: driving risks | fusion of judgments | selection of experts | regression

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.
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