In various examples, a sequential deep neural network (DNN) may be trained using ground truth data generated by correlating (e.g., by cross-sensor fusion) sensor data with image data representative of a sequences of images. In deployment, the sequential DNN may leverage the sensor correlation to compute various predictions using image data alone. The predictions may include velocities, in world space, of objects in fields of view of an ego-vehicle, current and future locations of the objects in image space, and/or a time-to-collision (TTC) between the objects and the ego-vehicle. These predictions may be used as part of a perception system for understanding and reacting to a current physical environment of the ego-vehicle.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Temporal information prediction in autonomous machine applications


    Beteiligte:
    WU YUE (Autor:in) / JANIS PEKKA (Autor:in) / TONG XIN (Autor:in) / YANG CHENG-CHIEH (Autor:in) / PARK MINWOO (Autor:in) / NISTER DAVID (Autor:in)

    Erscheinungsdatum :

    2023-12-26


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Klassifikation :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G05D SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES , Systeme zum Steuern oder Regeln nichtelektrischer veränderlicher Größen / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G06T Bilddatenverarbeitung oder Bilddatenerzeugung allgemein , IMAGE DATA PROCESSING OR GENERATION, IN GENERAL / G06V



    TEMPORAL INFORMATION PREDICTION IN AUTONOMOUS MACHINE APPLICATIONS

    WU YUE / JANIS PEKKA / TONG XIN et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    Temporal information prediction in autonomous machine applications

    WU YUE / JANIS PEKKA / TONG XIN et al. | Europäisches Patentamt | 2020

    Freier Zugriff

    Future trajectory prediction in multi-actor environment for autonomous machine applications

    KAMENEV ANDREY / SMOLYANSKIY NIKOLAI / KULKARNI IMRAN et al. | Europäisches Patentamt | 2022

    Freier Zugriff


    Trajectory prediction for autonomous driving based on multiscale spatial‐temporal graph

    Tang, Luqi / Yan, Fuwu / Zou, Bin et al. | Wiley | 2023

    Freier Zugriff