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

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

Received: 5 September 2012; in revised form: 14 October 2012 / Accepted: 29 October 2012 / Published: 2 November 2012
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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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MDPI and ACS Style

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.

AMA Style

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.

Chicago/Turabian Style

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.

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