Detection and tracking of road lanes is vital to a wide range of driver assistance systems. Confidence measures on tracked lanes are necessary to create reliable assistance systems. On curvy, multi-lane roads, adaptive cruise control systems could greatly benefit from information of lane location and curvature to identify the leading vehicle.We present a novel approach to estimating unobstructed lane length using visual lane confidence measures. The gradient between individually calculated lane confidence segments of a lane model provided by a lane tracker is used to estimate the distance to leading cars or obstacles on lanes. To achieve this goal, various confidence measures are defined, and a confidence fusion method based on Dempster Shafer is presented.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Lane confidence fusion for visual occupancy estimation


    Contributors:
    Gumpp, T. (author) / Nienhuser, D. (author) / Zollner, J. M. (author)


    Publication date :

    2011-06-01


    Size :

    1284369 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Lane Confidence Fusion for Visual Occupancy Estimation

    Gumpp, T. / Nienhuser, D. / Zollner, J.M. et al. | British Library Conference Proceedings | 2011


    Lane Occupancy Charges (Revised)

    D. G. Goulias / S. Yahalom / I. J. S. Chien | NTIS | 2001


    High Integrity Lane-level Occupancy Estimation of Road Obstacles Through LiDAR and HD Map Data Fusion

    Bernardi, Edoardo / Masi, Stefano / Xu, Philippe et al. | IEEE | 2020


    HIGH INTEGRITY LANE-LEVEL OCCUPANCY ESTIMATION OF ROAD OBSTACLES THROUGH LIDAR AND HD MAP DATA FUSION

    Bernardi, Edoardo / Masi, Stefano / Xu, Philippe et al. | British Library Conference Proceedings | 2020


    Evaluating Contraflow High-Occupancy Vehicle Lane Designs for Mitigating High-Occupancy Vehicle Lane Performance Degradation

    Wei, Zhensong / Hao, Peng / Barth, Matthew et al. | Transportation Research Record | 2022

    Free access