Systems and methods for utilizing interactive Gaussian processes for crowd navigation are provided. In one embodiment, a system for a crowd navigation of a host is provided. The system includes a processor, a statistical module, and a model module. The encoder receives the sensor data and context information. The encoder also extracts interaction patterns from observed trajectories from the sensor data and context information. The encoder further generates a static latent interaction graph for a first time step based on the interaction patterns. The recurrent generates a distribution of time dependent static latent interaction graphs iteratively from the first time step for a series of time steps based on the static latent interaction graph. The series of time steps are separated by a re-encoding gap. The decoder generates multi-modal distribution of future states based on the distribution of time dependent static latent interaction graphs.


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    Title :

    SYSTEMS AND METHODS FOR HETEROGENEOUS MULTI-AGENT MULTI-MODAL TRAJECTORY PREDICTION WITH EVOLVING INTERACTION GRAPHS


    Contributors:
    LI JIACHEN (author) / CHOI CHIHO (author)

    Publication date :

    2021-09-16


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung



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