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 pulicy is reaches using the fuctional neural network tool.


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    Title :

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


    Additional title:

    Eine hybride, auf einem neuronalen Netzwerk basierende Entscheidungsarchitektur: Anwendung für der Kollisionsvermeidung im Straßenverkehr


    Contributors:

    Published in:

    Optical Engineering ; 37 , 2 ; 370-377


    Publication date :

    1998


    Size :

    8 Seiten, 10 Bilder, 1 Tabelle, 19 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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