In this paper, a robust and real-time lane detection method is proposed. The method consists of two steps, the lane-marking detection and lane model fitting. After detecting the lane marking by the Intensity bump algorithm, the authors apply the post filters by constraining the parallelism of lane boundary. Then, a novel model fitting algorithm called Guided RANSAC is presented. The Guided RANSAC searches lanes from initial lane segments and the extrapolation of lane segments is used as the guiding information to elongate lane segments recursively. With the proposed method, the accuracy of the model fitting is greatly increased while the computational cost is reduced. Both theoretical and experimental analysis results are given to show the efficiency.


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    Titel :

    Lane detection based on guided RANSAC


    Weitere Titelangaben:

    Fahrspurerkennung basierend auf geführtem RANSAC


    Beteiligte:
    Hu, Yi (Autor:in) / Kim, You-Sun (Autor:in) / Lee, Kwang-Wook (Autor:in) / Ko, Sung-Jea (Autor:in)


    Erscheinungsdatum :

    2010


    Format / Umfang :

    4 Seiten, 7 Quellen



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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