High definition (HD) map data is a key feature to enable highly automated driving. With the advent of highly automated vehicles, car makers and map suppliers investigate new approaches to create and maintain HD maps by using on-board sensor data of series vehicles. While state-of-the-art-approaches focus on position and speed data analysis, the consideration of additional vehicle sensor data allows for novel approaches in the context of HD maps. By 2020, more than 30 million connected vehicles are expected to be sold per year, which will generate millions of terabytes of vehicular probe data. One of the major upcoming research issues is to find methods to exploit that probe data to generate and maintain HD maps. In this paper, we address how to develop such methods. We introduce a scalable infrastructure, which supports the ingestion, management and analysis of huge amounts of probe data. It supports an iterative process to develop, assess and tune methods for generating HD maps from probe data. We present a metric to assess methods regarding resulting map precision. As a proof of concept, we present an approach to derive road geometry of highways from location and sensor information.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deriving HD maps for highly automated driving from vehicular probe data


    Beteiligte:
    Massow, K. (Autor:in) / Kwella, B. (Autor:in) / Pfeifer, N. (Autor:in) / Hausler, F. (Autor:in) / Pontow, J. (Autor:in) / Radusch, I. (Autor:in) / Hipp, J. (Autor:in) / Dolitzscher, F. (Autor:in) / Haueis, M. (Autor:in)


    Erscheinungsdatum :

    2016-11-01


    Format / Umfang :

    2166697 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    SYSTEMS AND METHODS FOR UPDATING HIGHLY AUTOMATED DRIVING MAPS

    MA TENG / QU XIAOZHI / LI BAOLI | Europäisches Patentamt | 2019

    Freier Zugriff


    Deriving Forest/Non-Forest Maps from TanDEM-X Interferometric SAR Data

    Rizzoli, Paola / Martone, Michele / Wecklich, Christopher et al. | Deutsches Zentrum für Luft- und Raumfahrt (DLR) | 2017

    Freier Zugriff

    The importance of highly precise maps and location intelligence for automated driving

    Bueltmann,M. / Fastenrath,B. / HERE Deutschland,Berlin,DE | Kraftfahrwesen | 2016