This paper presents an advanced road course prediction algorithm focusing on longer distances. It shows how to simply combine the different sensors available in modern cars for a road course estimation task. Concretely, a digital-map-based estimation is fused with an optical lane recognition system. Both sensors are evaluated on a representative subset of test sequences to characterize their measurement uncertainties. Then a Bayesian fusion system combines the advantages of the single sensors. Extensive evaluations with high precision ground truth data demonstrate the feasibility of this approach.


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    Titel :

    Probabilistic fusion of rural road course estimations


    Beteiligte:


    Erscheinungsdatum :

    2013-10-01


    Format / Umfang :

    1271856 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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