The accurate classification of moving object in urban traffic scenarios is a key element for safe decision-making of intelligent vehicles. Multi-sensor approaches are typically based on features or specific objects models, which results in either an intrinsic lack of robustness or a significant design complexity. This paper proposes to take advantage of a Bayesian occupancy framework for perception, introducing a classifier combining grid-based footprints and speed estimations. The preliminary results obtained with a Lidar under a very realistic simulation framework are very promising.


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    Title :

    Footprint-based classification of road moving objects using occupancy grids


    Contributors:


    Publication date :

    2017-06-01


    Size :

    1693254 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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