This paper presents a lane estimation technique based on the particle filter framework, which fuses several image-based cues (edges, lane markings and curbs), and 3D cues extracted from stereovision. A partition sampling-like approach is used to decouple pitch estimation from the rest of the parameter set, allowing the use of a significantly lower number of particles, and initialization samples are used for faster handling of discontinuous roads. We also introduce a measure for detection quality, for result validation. The resulted solution has proven to be a reliable and fast lane detector for difficult scenarios.


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    Title :

    A stereovision-based probabilistic lane tracker for difficult road scenarios


    Contributors:


    Publication date :

    2008-06-01


    Size :

    766676 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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