As the efficiency of road segmentation has a direct effect on the reliability of road following and planning -- and consequently the speed of the Autonomous Land Vehicle (ALV) -- road segmentation is one of the most preliminary and important tasks for the road following and planning of ALV, and a variety of methods for color road segmentation have been proposed. This presentation proposes a new data-fusion-based color road segmentation method in which a pyramid-based data structure and the corresponding region splitting and combination techniques for the classification of sensed areas are adopted. In the segmentation process, the roads are first segmented in two 1-D color spaces, and the data fusion technique is then used to combine the two classification results, improving the accuracy of the road segmentation.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Color road segmentation for autonomous land vehicle (ALV) road following


    Contributors:
    Liu, Lei-Jian (author) / Wu, Yong-Ge (author) / Liu, Ke (author) / Yang, Jingyu (author)

    Conference:

    Mobile Robots VIII ; 1993 ; Boston,MA,United States


    Published in:

    Publication date :

    1994-02-01





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Color Image Segmentation for Autonomous Land Vehicle Road Following

    Fernandez, J. / Frau, J. | British Library Conference Proceedings | 1994


    Expert Vision System for Autonomous Land Vehicle Road Following

    S. J. Dickinson / L. S. Davis | NTIS | 1988


    Vision-based road following for an autonomous land vehicle

    WAXMAN, A. / LE MOIGNE, J. / SRINIVASAN, B. et al. | AIAA | 1985


    Vision-based road following in the autonomous land vehicle

    Seida, S. / Morgenthaler, D.G. / Podlaseck, M. et al. | Tema Archive | 1987


    Autonomous Vehicle Following System In Off-road Environment

    Yang, Shuaicong / Fu, Mengyin / Yang, Yi et al. | IEEE | 2020