In various examples, a multi-sensor fusion machine learning model—such as a deep neural network (DNN)—may be deployed to fuse data from a plurality of individual machine learning models. As such, the multi-sensor fusion network may use outputs from a plurality of machine learning models as input to generate a fused output that represents data from fields of view or sensory fields of each of the sensors supplying the machine learning models, while accounting for learned associations between boundary or overlap regions of the various fields of view of the source sensors. In this way, the fused output may be less likely to include duplicate, inaccurate, or noisy data with respect to objects or features in the environment, as the fusion network may be trained to account for multiple instances of a same object appearing in different input representations.


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    Titel :

    SENSOR FUSION FOR AUTONOMOUS MACHINE APPLICATIONS USING MACHINE LEARNING


    Beteiligte:
    PARK MINWOO (Autor:in) / KWON JUNGHYUN (Autor:in) / KOCAMAZ MEHMET K (Autor:in) / SEO HAE-JONG (Autor:in) / RODRIGUEZ HERVAS BERTA (Autor:in) / CHOE TAE EUN (Autor:in)

    Erscheinungsdatum :

    30.12.2021


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Klassifikation :

    IPC:    G06K Erkennen von Daten , RECOGNITION OF DATA / B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G06T Bilddatenverarbeitung oder Bilddatenerzeugung allgemein , IMAGE DATA PROCESSING OR GENERATION, IN GENERAL



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