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    OCCUPANCY CLUSTERING ACCORDING TO RADAR DATA
    11.
    发明申请
    OCCUPANCY CLUSTERING ACCORDING TO RADAR DATA 有权

    公开(公告)号：US20220317302A1

    公开(公告)日：2022-10-06

    申请号：US17220394

    申请日：2021-04-01

    Applicant: QUALCOMM Incorporated

    Inventor： Volodimir SLOBODYANYUK ,  Avdhut JOSHI ,  Sundar SUBRAMANIAN

    IPC: G01S17/89 ,  G01S17/931 ,  G05D1/02

    Abstract: In some aspects, a device may receive, from a radar scanner or a LIDAR scanner of a vehicle, point data that identifies a first point and a second point. The device may receive grid information that identifies cells of a grid that is associated with mapping a physical environment of the vehicle. The device may designate, based on determining that a distance between the first point and the second point satisfies a distance threshold, a subset of the cells as an occupied cluster that is associated with the first point and the second point. The device may perform an action associated with the vehicle based on location information associated with the occupied cluster. Numerous other aspects are described.

    DYNAMIC OCCUPANCY GRID WITH CAMERA INTEGRATION
    13.
    发明申请
    DYNAMIC OCCUPANCY GRID WITH CAMERA INTEGRATION 有权

    公开(公告)号：US20250130329A1

    公开(公告)日：2025-04-24

    申请号：US18812289

    申请日：2024-08-22

    Applicant: QUALCOMM Incorporated

    Inventor： Makesh Pravin JOHN WILSON ,  Vishnuu APPAYA DHANABALAN ,  Volodimir SLOBODYANYUK ,  Avdhut JOSHI ,  Radhika Dilip GOWAIKAR

    IPC: G01S13/931 ,  G01S13/72 ,  G01S13/89

    Abstract: A dynamic occupancy grid determination method includes: obtaining, at an apparatus, at least one radar-based occupancy grid based on radar sensor measurements, each of the at least one radar-based occupancy grid comprising a plurality of first cells, each cell of the plurality of first cells having a corresponding first occupancy probability and first velocity; obtaining, at the apparatus, at least one camera-based occupancy grid based on camera measurements, each of the at least one camera-based occupancy grid comprising a plurality of second cells, each cell of the plurality of second cells having a corresponding second occupancy probability and second velocity; and determining, at the apparatus, a dynamic occupancy grid by analyzing the at least one radar-based occupancy grid and the at least one camera-based occupancy grid.

    PROCESSING FOR MACHINE LEARNING BASED OBJECT DETECTION USING SENSOR DATA
    14.
    发明公开
    PROCESSING FOR MACHINE LEARNING BASED OBJECT DETECTION USING SENSOR DATA 审中-公开

    公开(公告)号：US20240221186A1

    公开(公告)日：2024-07-04

    申请号：US18530660

    申请日：2023-12-06

    Applicant: QUALCOMM Incorporated

    Inventor： Makesh Pravin JOHN WILSON ,  Radhika Dilip GOWAIKAR ,  Shantanu Chaisson SANYAL ,  Avdhut JOSHI ,  Rex JOMY JOSEPH ,  Volodimir SLOBODYANYUK

    IPC: G06T7/246

    CPC classification number: G06T7/251 ,  G06T2207/10028 ,  G06T2207/20084

    Abstract: In some aspects, a device may obtain sensor data associated with identifying measured properties of an object in an environment. The device may detect a trigger event associated with at least one of the environment or the device. The device may modify, based on detecting the trigger event, one or more pre-processing operations associated with the sensor data for input to a neural network, and/or one or more post-processing operations associated with an object detection output of the neural network. The device may perform the one or more pre-processing operations associated with the sensor data to generate pre-processed sensor data. The device may generate the object detection output for the object based on detecting the object using the pre-processed sensor data as the input to the neural network. The device may perform the one or more post-processing operations using the object detection output. Numerous other aspects are described.

    RADAR CLUSTERING USING MACHINE LEARNING
    16.
    发明公开
    RADAR CLUSTERING USING MACHINE LEARNING 审中-公开

    公开(公告)号：US20230366983A1

    公开(公告)日：2023-11-16

    申请号：US17744566

    申请日：2022-05-13

    Applicant: QUALCOMM Incorporated

    Inventor： Shubham Sanjeev GARG ,  Amir SALIMI ,  Jayakrishnan UNNIKRISHNAN ,  Avdhut JOSHI

    IPC: G01S7/41 ,  G06N3/08 ,  G06N3/04 ,  G01S13/66

    CPC classification number: G01S7/417 ,  G06N3/08 ,  G06N3/04 ,  G01S13/66

    Abstract: A processor-implemented method for radar-based tracking of an object includes transmitting radio frequency (RF) signals. In response to the transmitted RF signals, one or more return RF signals are received. Features of the one or more return RF signals are extracted. A graph comprising multiple nodes is generated. Each node of the graph corresponds to the one or more return RF signals and indicates a potential target object detection. An existence of a plurality of edges is determined. Each edge connects a pair of nodes in the graph based on features of the return RF signals. The existence of each edge indicates that the pair of nodes connected correspond to a same target object.

    OCCUPANCY MAPPING FOR AUTONOMOUS CONTROL OF A VEHICLE
    17.
    发明申请
    OCCUPANCY MAPPING FOR AUTONOMOUS CONTROL OF A VEHICLE 有权

    公开(公告)号：US20230036838A1

    公开(公告)日：2023-02-02

    申请号：US17443974

    申请日：2021-07-29

    Applicant: QUALCOMM Incorporated

    Inventor： Volodimir SLOBODYANYUK ,  Avdhut JOSHI ,  Sundar SUBRAMANIAN

    IPC: B60W40/08 ,  B60W60/00

    Abstract: In some aspects, a device may receive point data associated with a cell of an occupancy grid for controlling a vehicle. The device may determine, based on the point data, a characteristic of the cell that is associated with an occupancy probability of the cell, wherein the occupancy probability is determined according to a first technique based on the point data. The device may configure, based on the characteristic, the occupancy probability for the cell, within the occupancy grid, according to a second technique. Numerous other aspects are described.

    STATIC OCCUPANCY TRACKING
    18.
    发明申请
    STATIC OCCUPANCY TRACKING 有权

    公开(公告)号：US20220237402A1

    公开(公告)日：2022-07-28

    申请号：US17452552

    申请日：2021-10-27

    Applicant: QUALCOMM Incorporated

    Inventor： Jayakrishnan UNNIKRISHNAN ,  Yoga Y NADARAAJAN ,  Avdhut JOSHI

    IPC: G06K9/00 ,  G06K9/62 ,  G06N3/02

    Abstract: Techniques and systems are provided for determining static occupancy. For example, an apparatus can be configured to determine one or more pixels associated with one or more static objects depicted in one or more images of a three-dimensional space. The apparatus can be configured to obtain a point map including a plurality of map points, the plurality of map points corresponding to a portion of the three-dimensional space. The apparatus can be configured to determine, based on the point map and the one or more pixels associated with the one or more static objects, a probability of occupancy by the one or more static objects in the portion of the three-dimensional space. The apparatus can be configured to combine information across multiple images of the three-dimensional space, and can determine probabilities of occupancy for all cells in a static occupancy grid that is associated with the three-dimensional space.

    EFFECTIVE LEVERAGING OF SYNTHETIC DATA FOR DEPTH ESTIMATION MACHINE LEARNING MODELS
    20.
    发明申请
    EFFECTIVE LEVERAGING OF SYNTHETIC DATA FOR DEPTH ESTIMATION MACHINE LEARNING MODELS 有权

    公开(公告)号：US20240371015A1

    公开(公告)日：2024-11-07

    申请号：US18311640

    申请日：2023-05-03

    Applicant: QUALCOMM Incorporated

    Inventor： Amin ANSARI ,  Sai Madhuraj JADHAV ,  Avdhut JOSHI

    IPC: G06T7/50 ,  G06N20/00

    Abstract: Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. Data from a source domain and data from a target domain is accessed. A set of machine learning models is trained, based on the data from the source domain and the data from the target domain, to generate depth outputs based on input images. Training the set of machine learning models includes: generating a discriminator output based at least in part on an input image frame from either the source domain or the target domain, generating an adversarial loss based on the discriminator output, and refining one or more machine learning models of the set of machine learning models based on the adversarial loss.

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