A research work was carried out at the Centre for Computational Intelligence at NTU to develop novel technologies for the routing, navigation, and control of intelligent cars. One objective is to endow the cars with the ability to autonomously drive on various types of roads and realize manoeuvres such as reverse and parallel parking, three-point turns, etc. The approach is to design a self-training system that makes use of human expertise to automatically derive a working car control system. A new neuro-fuzzy architecture known as the GenSoYager Fuzzy Neural Network has been realized and integrated with a car-driving simulator for training and testing purposes. The GenSoYagerFNN has proven so far superior to other trained networks in detecting parking slots and accomplishing reverse parking manoeuvres. The approach described has also been validated using a microprocessor controlled model car.


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    Titel :

    Self-trained automated parking system


    Weitere Titelangaben:

    Ein selbstlernendes, automatisches Parksystem


    Beteiligte:
    Oentaryo, R.J. (Autor:in) / Pasquier, M. (Autor:in)


    Erscheinungsdatum :

    2004


    Format / Umfang :

    6 Seiten, 11 Quellen




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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