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.


    Access

    Download


    Export, share and cite



    Title :

    USING DEEP LEARNING TO IDENTIFY ROAD GEOMETRY FROM POINT CLOUDS


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

    Publication date :

    2025-04-10


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    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



    Road extraction from mobile laser scanning point clouds

    Tao, Wang | British Library Conference Proceedings | 2022


    Road Markings Segmentation from LIDAR Point Clouds using Reflectivity Information

    Certad, Novel / Morales-Alvarez, Walter / Olaverri-Monreal, Cristina | IEEE | 2022


    Deep Learning-Based Damage Detection from Aerial SfM Point Clouds

    Mohammad Ebrahim Mohammadi / Daniel P. Watson / Richard L. Wood | DOAJ | 2019

    Free access


    Automated 3D Road Boundary Extraction and Vectorization Using MLS Point Clouds

    Mi, Xiaoxin / Yang, Bisheng / Dong, Zhen et al. | IEEE | 2022