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    FINETUNING FOR MULTI-TASK LEARNING
    1.
    发明公开
    FINETUNING FOR MULTI-TASK LEARNING 审中-公开

    公开(公告)号：US20240256860A1

    公开(公告)日：2024-08-01

    申请号：US18424024

    申请日：2024-01-26

    Applicant: Mobileye Vision Technologies Ltd.

    Inventor： Shai Shalev-Shwartz ,  Eran Malach ,  Gal Kaplun ,  Andrey Gurevich ,  Mazor David ,  Tal Swisa

    IPC: G06N3/08

    CPC classification number: G06N3/08

    Abstract: Systems and methods generate an extended trained model. In one implementation, a method includes obtaining a preexisting trained model, the preexisting trained model including a plurality of preexisting weights, wherein each of the plurality of preexisting weights is associated with a preexisting value; identifying a subset of the plurality of preexisting weights; generating a plurality of extended weights based on a training process using duplicates of the subset of the plurality of preexisting weights; and generating the extended trained model, wherein the extended trained model includes the plurality of preexisting weights and the plurality of extended weights.

    Road vector fields
    2.
    发明授权
    Road vector fields 有权

    公开(公告)号：US12151685B2

    公开(公告)日：2024-11-26

    申请号：US17482048

    申请日：2021-09-22

    Applicant: MOBILEYE VISION TECHNOLOGIES LTD.

    Inventor： Eran Malach ,  Shai Shalev-Shwartz ,  Dor Noti ,  Ziv Cohen

    IPC: G01C21/00 ,  B60W40/06 ,  B60W50/00 ,  B60W60/00 ,  G06N3/02 ,  G06T7/70 ,  G06T7/73 ,  G06V10/44 ,  G06V20/56

    Abstract: Systems and methods are provided for vehicle navigation. In one implementation, at least one processor may receive, from a camera of a vehicle, at least one image captured from an environment of the vehicle. The processor may analyze the at least one image to identify a road topology feature in the environment of the vehicle represented in the at least one image and at least one point associated with the at least one image. Based on the identified road topology feature, the processor may determine an estimated path in the environment of the vehicle associated with the at least one point. The processor may further cause the vehicle to implement a navigational action based on the estimated path.

    FULL IMAGE DETECTION
    3.
    发明申请
    FULL IMAGE DETECTION 有权

    公开(公告)号：US20220027642A1

    公开(公告)日：2022-01-27

    申请号：US17499291

    申请日：2021-10-12

    Applicant: MOBILEYE VISION TECHNOLOGIES LTD.

    Inventor： YAAKOV SHAMBIK ,  Ohad Chitrit ,  Eran Malach ,  Dan Kufra

    IPC: G06K9/00 ,  G06T7/50 ,  G06T7/70 ,  G06T7/20

    Abstract: Systems and methods are provided for vehicle navigation. In one implementation, at least one processing device may receive, from a camera of the host vehicle, at least one captured image representative of an environment of the host vehicle. The processing device may analyze one or more pixels of the at least one captured image to determine whether the one or more pixels represent at least a portion of a target vehicle. For pixels determined to represent at least a portion of the target vehicle, the processing device may determine one or more estimated distance values from the one or more pixels to at least one edge of a face of the target vehicle; and generate, based on the analysis of the one or more pixels, including the determined one or more distance values associated with the one or more pixels, at least a portion of a boundary relative to the target vehicle.

    Fast CNN classification of multi-frame semantic signals
    4.
    发明授权
    Fast CNN classification of multi-frame semantic signals 有权

    公开(公告)号：US11200468B2

    公开(公告)日：2021-12-14

    申请号：US16685925

    申请日：2019-11-15

    Applicant: Mobileye Vision Technologies Ltd.

    Inventor： Eran Malach ,  Yaakov Shambik ,  Jacob Bentolila ,  Idan Geller

    IPC: G06K9/00 ,  G06K9/72 ,  B60R11/04 ,  G05D1/00 ,  G05D1/02 ,  G06K9/62 ,  G06N3/04

    Abstract: The present subject matter provides various technical solutions to technical problems facing advanced driver assistance systems (ADAS) and autonomous vehicle (AV) systems. In particular, disclosed embodiments provide systems and methods that may use cameras and other sensors to detect objects and events and identify them as predefined signal classifiers, such as detecting and identifying a red stoplight. These signal classifiers are used within ADAS and AV systems to control the vehicle or alert a vehicle operator based on the type of signal. These ADAS and AV systems may provide full vehicle operation without requiring human input. The embodiments disclosed herein provide systems and methods that can be used as part of or in combination with ADAS and AV systems.

    ROAD VECTOR FIELDS
    6.
    发明申请
    ROAD VECTOR FIELDS 有权

    公开(公告)号：US20220009518A1

    公开(公告)日：2022-01-13

    申请号：US17482854

    申请日：2021-09-23

    Applicant: Mobileye Vision Technologies Ltd.

    Inventor： Eran Malach ,  Shai Shalev-Shwartz ,  Dor Noti ,  Ziv Cohen

    IPC: B60W60/00 ,  G01C21/00 ,  B60W50/00 ,  G06K9/00

    Abstract: Systems and methods are provided for vehicle navigation. In one implementation, at least one processor may receive, from a camera of a vehicle, at least one image captured from an environment of the vehicle. The processor may analyze the at least one image to identify a road topology feature in the environment of the vehicle represented in the at least one image and at least one point associated with the at least one image. Based on the identified road topology feature, the processor may determine an estimated path in the environment of the vehicle associated with the at least one point. The processor may further cause the vehicle to implement a navigational action based on the estimated path.

    FAST CNN CLASSIFICATION OF MULTI-FRAME SEMANTIC SIGNALS
    7.
    发明申请
    FAST CNN CLASSIFICATION OF MULTI-FRAME SEMANTIC SIGNALS 审中-公开

    公开(公告)号：US20200160126A1

    公开(公告)日：2020-05-21

    申请号：US16685925

    申请日：2019-11-15

    Applicant: Mobileye Vision Technologies Ltd.

    Inventor： Eran Malach ,  Yaakov Shambik ,  Jacob Bentolila ,  Idan Geller

    IPC: G06K9/72 ,  G06K9/00 ,  G06K9/62 ,  G06N3/04 ,  B60R11/04 ,  G05D1/02 ,  G05D1/00

    Abstract: The present subject matter provides various technical solutions to technical problems facing advanced driver assistance systems (ADAS) and autonomous vehicle (AV) systems. In particular, disclosed embodiments provide systems and methods that may use cameras and other sensors to detect objects and events and identify them as predefined signal classifiers, such as detecting and identifying a red stoplight. These signal classifiers are used within ADAS and AV systems to control the vehicle or alert a vehicle operator based on the type of signal. These ADAS and AV systems may provide full vehicle operation without requiring human input. The embodiments disclosed herein provide systems and methods that can be used as part of or in combination with ADAS and AV systems.

    APPARATUS, SYSTEM, AND METHOD OF TRAINING A MACHINE LEARNING (ML) MODEL
    8.
    发明申请
    APPARATUS, SYSTEM, AND METHOD OF TRAINING A MACHINE LEARNING (ML) MODEL 有权

    公开(公告)号：US20240394598A1

    公开(公告)日：2024-11-28

    申请号：US18666306

    申请日：2024-05-16

    Applicant: MobilEye Vision Technologies Ltd.

    Inventor： Etay Zvi Livne ,  Gal Kaplun ,  Eran Malach ,  Shai Shalev-Shwartz

    IPC: G06N20/00

    Abstract: For example, a Machine-Learning (ML) model training system may be configured to shuffle a plurality of input examples in plurality of input blocks to provide a plurality of first-shuffled examples in a plurality of shuffled blocks; and to provide the plurality of first-shuffled examples in the plurality of shuffled blocks as an input to a model training procedure to train an ML model. For example, the model training procedure may include a plurality of epoch iterations applied to a plurality of block groups. For example, an epoch iteration may include determining a block group for the epoch iteration by randomly selecting a group of shuffled blocks from the plurality of shuffled blocks; shuffling first-shuffled examples in the block group to provide a plurality of second-shuffled examples; and updating the ML model according to a plurality of update iterations applied to the plurality of second-shuffled examples.

    FAST CNN CLASSIFICATION OF MULTI-FRAME SEMANTIC SIGNALS
    10.
    发明申请
    FAST CNN CLASSIFICATION OF MULTI-FRAME SEMANTIC SIGNALS 有权

    公开(公告)号：US20220058453A1

    公开(公告)日：2022-02-24

    申请号：US17519404

    申请日：2021-11-04

    Applicant: Mobileye Vision Technologies Ltd.

    Inventor： Eran Malach ,  Yaakov Shambik ,  Jacob Bentolila ,  Idan Geller

    IPC: G06K9/72 ,  B60R11/04 ,  G05D1/00 ,  G05D1/02 ,  G06K9/00 ,  G06K9/62 ,  G06N3/04

    Abstract: The present subject matter provides various technical solutions to technical problems facing advanced driver assistance systems (ADAS) and autonomous vehicle (AV) systems. In particular, disclosed embodiments provide systems and methods that may use cameras and other sensors to detect objects and events and identify them as predefined signal classifiers, such as detecting and identifying a red stoplight. These signal classifiers are used within ADAS and AV systems to control the vehicle or alert a vehicle operator based on the type of signal. These ADAS and AV systems may provide full vehicle operation without requiring human input. The embodiments disclosed herein provide systems and methods that can be used as part of or in combination with ADAS and AV systems.

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