The realization of on- and off-road autonomous navigation of Unmanned Ground Vehicles (UGVs) requires real-time motion planning in the presence of dynamic objects with unknown trajectories. To successfully plan paths and to navigate in an unstructured environment, the UGVs should have the difficult and computationally intensive competency to predict the future locations of moving objects that could interfere with its path. This paper details the development of a combined probabilistic object classification and estimation theoretic framework to predict the future location of moving objects, along with an associated uncertainty measure. The development of a moving object testbed that facilitates the testing of different representations and prediction algorithms in an implementation-independent platform is also outlined.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Moving object prediction for off-road autonomous navigation


    Contributors:

    Conference:

    Unmanned Ground Vehicle Technology V ; 2003 ; Orlando,Florida,United States


    Published in:

    Publication date :

    2003-09-30





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    PREDICTION OF ROAD GRADE FOR AUTONOMOUS VEHICLE NAVIGATION

    PULLAGURLA HARISH / MILLER ZACHARY / CUNNINGHAM ANDREW | European Patent Office | 2025

    Free access

    AUTONOMOUS NAVIGATION OF ROAD INTERSECTIONS

    LIN SHU-KAI / ANG HIAP LEE / HERBST EVAN | European Patent Office | 2025

    Free access

    Autonomous navigation based on road signatures

    STEIN GIDEON / SPRINGER OFER / FERENCZ ANDRAS | European Patent Office | 2021

    Free access

    AUTONOMOUS NAVIGATION BASED ON ROAD SIGNATURES

    STEIN GIDEON / SPRINGER OFER / FERENCZ ANDRAS | European Patent Office | 2017

    Free access

    Road Boundary Detection for Autonomous Vehicle Navigation

    Davis, Larry S. / Kushner, Todd R. | SPIE | 1985