Hybrid neural-based decision level fusion architecture: application to road traffic collision avoidance

Kurosh Madani; Abdennasser Chebira; Kamel Bouchefra; Thierry Maurin; Roger Reynaud

doi:10.1117/1.602015

1 February 1998 Hybrid neural-based decision level fusion architecture: application to road traffic collision avoidance

Kurosh Madani, Abdennasser Chebira, Kamel Bouchefra, Thierry Maurin, Roger Reynaud

Optical Engineering, Vol. 37, Issue 2, (February 1998). https://doi.org/10.1117/1.602015

A hybrid decision level architecture for a road collision risks avoidance system is presented. The goal of the decision level is to classify the behavior of the vehicles observed by a smart system or vehicle. The knowledge of vehicle behavior enables the best management of the smart system resources. The association of a model to each observed vehicle mainly enables the limitation of inference and of the set of actions to be activated; thus the interactions between system levels can be more intelligent. The decision level of this architecture is composed of a neural classifier, which is associated to a numerical classifier. Each of these classifiers provides decisions that are expressed within the framework of fuzzy theory. An optimal fusion policy is reached using the functional neural network tool.

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Kurosh Madani, Abdennasser Chebira, Kamel Bouchefra, Thierry Maurin, and Roger Reynaud "Hybrid neural-based decision level fusion architecture: application to road traffic collision avoidance," Optical Engineering 37(2), (1 February 1998). https://doi.org/10.1117/1.602015

Published: 1 February 1998

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