Lidar produces three-dimensional point clouds. From the point clouds, objects need to be detected and tracked. For example, from the point clouds, embodiments detect what kind of objects is detected, what shape the object is, and the objects current location and trajectory. Each lidar sensor on a vehicle produces a point cloud periodically. The point cloud is input into a deep learning neural network that outputs road geometry, such as road edges and lane dividers. In some embodiments, the other network can also output information about other objects in the environment, such as other vehicles and pedestrians.


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    Titel :

    USING DEEP LEARNING TO IDENTIFY ROAD GEOMETRY FROM POINT CLOUDS


    Beteiligte:
    MOSKOWITZ JEFFREY (Autor:in) / BRAGINSKY BORIS (Autor:in) / BOUBLIL DAVID (Autor:in) / HAZUT SHMUEL (Autor:in) / ZIV ALON (Autor:in) / GUBERMAN YAHEL (Autor:in) / YAKOV AVNER (Autor:in) / KEREN NIV (Autor:in) / NAILAND ARYEH (Autor:in) / KAPLAN ADAM (Autor:in)

    Erscheinungsdatum :

    10.04.2025


    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 / G01S RADIO DIRECTION-FINDING , Funkpeilung



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