This paper addresses the problem of finding the host vehicle's lateral position on a multi-lane road, using information obtained by processing video sequences. A very important cue for lane identification is the class of the boundaries of the current lane. This paper presents a reliable solution for lane boundary type identification, based on frequency analysis of the gray level profile of these boundaries, assuming that the current lane is already detected. The lane boundary information is combined with the obstacle information, through a Bayesian Network which will output, frame by frame, the probability of the vehicle to be positioned on each lane of the road. The probability result will be propagated throughout the sequence by a Particle Filter.


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    Title :

    On-road position estimation by probabilistic integration of visual cues


    Contributors:


    Publication date :

    2012-06-01


    Size :

    1189816 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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