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    Real-time recognition of large-scale driving patterns

    New search for: Engstrom, J.
    New search for: Victor, T.

    This work has explored the possibilities of real-time, automatic, recognition of large-scale driving situations. Employing a statistical pattern recognition framework, implemented by means of feedforward neural networks, models were developed for recognising, from vehicle control data, four classes of driving situations: highway, main road, suburban traffic and city traffic.

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    Real-time recognition of large-scale driving patterns

    New search for: Engstrom, J.
    New search for: Victor, T.

    This work has explored the possibilities of real-time, automatic, recognition of large-scale driving situations. Employing a statistical pattern recognition framework, implemented by means of feedforward neural networks, models were developed for recognising, from vehicle control data, four classes of driving situations: highway, main road, suburban traffic and city traffic.

    Access

    Check access
    Check availability in my library
    Order at Subito €

    Real-time recognition of large-scale driving patterns

    New search for: Engstrom, J.
    New search for: Victor, T.

    This work has explored the possibilities of real-time, automatic, recognition of large-scale driving situations. Employing a statistical pattern recognition framework, implemented by means of feedforward neural networks, models were developed for recognising, from vehicle control data, four classes of driving situations: highway, main road, suburban traffic and city traffic.

    Access

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    Check availability in my library
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    Document information
    Title:

    Real-time recognition of large-scale driving patterns

    Contributors:

    Engstrom, J. (author) / Victor, T. (author)

    Published in:

    ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585) ; 1018-1023

    Publication date:

    2001-01-01

    Size:

    510230 byte

    ISBN:

    0-7803-7194-1

    DOI:

    https://doi.org/10.1109/ITSC.2001.948801

    Type of media:

    Conference paper

    Type of material:

    Electronic Resource

    Language:

    English

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