Accurate and robust lane results are of great significance in any driving-assistance system. To achieve robustness and accuracy in difficult scenarios, probabilistic estimation techniques are needed to compensate for the errors in the detection of lane-delimiting features. This paper presents a solution for lane estimation in difficult scenarios based on the particle-filtering framework. The solution employs a novel technique for pitch detection based on the fusion of two stereovision-based cues, a novel method for particle measurement and weighing using multiple lane-delimiting cues extracted by grayscale and stereo data processing, and a novel method for deciding upon the validity of the lane-estimation results. Initialization samples are used for uniform handling of the road discontinuities, eliminating the need for explicit track initialization. The resulting solution has proven to be a reliable and fast lane detector for difficult scenarios.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Probabilistic Lane Tracking in Difficult Road Scenarios Using Stereovision


    Contributors:
    Danescu, R. (author) / Nedevschi, S. (author)


    Publication date :

    2009-06-01


    Size :

    1013261 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    A stereovision-based probabilistic lane tracker for difficult road scenarios

    Danescu, Radu / Nedevschi, Sergiu / Meinecke, Marc-Michael et al. | IEEE | 2008


    A Stereovision-Based Probabilistic Lane Tracker for Difficult Road Scenarios

    Danescu, R.G. / Nedevschi, S. / Meinecke, M.-M. et al. | British Library Conference Proceedings | 2008


    New Results in Stereovision Based Lane Tracking

    Danescu, R.G. / Nedevschi, S. / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2011


    Stereovision-based road boundary detection for intelligent vehicles in challenging scenarios

    Guo, Chunzhao / Mita, Seiichi / McAllester, David | Tema Archive | 2009