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

Neural networks

Collision avoidance

Neurons

Databases

Roads

Signal processing

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