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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    Titel :

    Footprint-based classification of road moving objects using occupancy grids


    Beteiligte:
    Alonso, Victor (Autor:in) / Correal, Raul (Autor:in) / Villagra, Jorge (Autor:in)


    Erscheinungsdatum :

    01.06.2017


    Format / Umfang :

    1693254 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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