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Sensors 2012, 12(11), 14711-14729; doi:10.3390/s121114711

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

1,* , 1
1 Department of Computer Architecture, University Rey Juan Carlos, Móstoles 28933, Spain 2 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 / Revised: 14 October 2012 / Accepted: 29 October 2012 / Published: 2 November 2012
(This article belongs to the Special Issue New Trends towards Automatic Vehicle Control and Perception Systems)
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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 driving risks; fusion of judgments; selection of experts; regression
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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.

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