This paper describes the obstacle detection and tracking algorithms developed for Boss, which is Carnegie Mellon University 's winning entry in the 2007 DARPA Urban Challenge. We describe the tracking subsystem and show how it functions in the context of the larger perception system. The tracking subsystem gives the robot the ability to understand complex scenarios of urban driving to safely operate in the proximity of other vehicles. The tracking system fuses sensor data from more than a dozen sensors with additional information about the environment to generate a coherent situational model. A novel multiple-model approach is used to track the objects based on the quality of the sensor data. Finally, the architecture of the tracking subsystem explicitly abstracts each of the levels of processing. The subsystem can easily be extended by adding new sensors and validation algorithms.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Obstacle Detection and Tracking for the Urban Challenge


    Beteiligte:
    Darms, M.S. (Autor:in) / Rybski, P.E. (Autor:in) / Baker, C. (Autor:in) / Urmson, C. (Autor:in)


    Erscheinungsdatum :

    2009-09-01


    Format / Umfang :

    378256 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Vehicle detection and tracking for the Urban Challenge

    Darms, Michael / Rybski, Paul E. / Urmson, Chris | Tema Archiv | 2008


    Vehicle detection and tracking for the Urban Challenge

    Darms,M. / Continental,Lindau,DE / Univ.of Pittsburgh,US | Kraftfahrwesen | 2008


    Multisensor obstacle detection and tracking

    Stiller, C. / Hipp, J. / Rossig, C. et al. | British Library Online Contents | 2000


    Urban rail train obstacle detection device

    CHEN CHENG | Europäisches Patentamt | 2021

    Freier Zugriff