The focus of this paper is to propose a concept for integrated detection, representation and interpretation of lanes and roads as well as their possible roles in the vehicle's surrounding. This includes a hierarchical probabilistic representation using particle approximation of multiple probability density functions for different levels of abstraction. Low- and high-level information can be integrated, leading to mutual bottom-up and top-down refinement of scene representation. Based on this representation bayesian networks are modeled for probabilistically inferring abstract, not directly observable relations. Based on these relations, a consistent subset of all hypotheses is generated to represent the current situation. The approach is highly flexible, able to integrate different information sources of varying levels of abstraction, while preserving a high level of probabilistic detail.


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    Titel :

    Probabilistic hierarchical detection, representation and scene interpretation of lanes and roads


    Beteiligte:
    Gumpp, Thomas (Autor:in) / Oberlander, Jan (Autor:in) / Marius Zollner, J. (Autor:in)


    Erscheinungsdatum :

    2012-07-01


    Format / Umfang :

    968511 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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