In various examples, a deep neural network (DNN) is trained to accurately predict, in deployment, distances to objects and obstacles using image data alone. The DNN may be trained with ground truth data that is generated and encoded using sensor data from any number of depth predicting sensors, such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. Camera adaptation algorithms may be used in various embodiments to adapt the DNN for use with image data generated by cameras with varying parameters—such as varying fields of view. In some examples, a post-processing safety bounds operation may be executed on the predictions of the DNN to ensure that the predictions fall within a safety-permissible range.


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    Titel :

    Distance to obstacle detection in autonomous machine applications


    Beteiligte:
    YANG YILIN (Autor:in) / JUJJAVARAPU BALA SIVA SASHANK (Autor:in) / JANIS PEKKA (Autor:in) / YE ZHAOTING (Autor:in) / OH SANGMIN (Autor:in) / PARK MINWOO (Autor:in) / HERRERA CASTRO DANIEL (Autor:in) / KOIVISTO TOMMI (Autor:in) / NISTER DAVID (Autor:in)

    Erscheinungsdatum :

    17.09.2024


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Klassifikation :

    IPC:    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 / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G06V



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