Localization is still one of the most challenging tasks in autonomous driving on city roads. Further development and improvement of automatic functions of vehicles in urban conditions are not possible without overcoming the problem of significant degradation of GNSS signal quality. The proposed approach to localization can provide information about vehicle position on the road in different operational conditions. Desired stability and quality are achieved by using the combination of conventional computer vision, neural networks and Kalman filtering.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Robust Localization of a Self-Driving Vehicle in a Lane


    Beteiligte:


    Erscheinungsdatum :

    2020-09-01


    Format / Umfang :

    1641078 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






    Robust particle filter for lane-precise localization

    Rabe, Johannes / Stiller, Christoph | IEEE | 2017


    Lane marking aided vehicle localization

    Tao, Z. / Bonnifait, Ph. / Fremont, V. et al. | IEEE | 2013