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    DESTINATION VALIDATION METHOD AND SYSTEM
    21.
    发明申请
    DESTINATION VALIDATION METHOD AND SYSTEM 有权

    公开(公告)号：US20240430643A1

    公开(公告)日：2024-12-26

    申请号：US18338718

    申请日：2023-06-21

    Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

    Inventor： Alaa M. Khamis ,  Yun Qian Miao ,  Russell A. Patenaude

    IPC: H04W4/029

    Abstract: A method for confirming arrival at a target location includes comparing, by one or more controllers, properties of a plurality of identifying features of the target location with properties of a corresponding plurality of corresponding features of a present location to result in a plurality of comparisons. The method additionally includes identifying, by one or more controllers, the present location as the target location if the plurality of comparisons concludes that the present location is the target location. The properties of a plurality of identifying features of the target location include at least one property of one or more wireless communication networks expected to be detectable at the target location.

    SYSTEMS AND METHODS FOR ENHANCED DISTANCE ESTIMATION BY A MONO-CAMERA USING RADAR AND MOTION DATA
    26.
    发明申请
    SYSTEMS AND METHODS FOR ENHANCED DISTANCE ESTIMATION BY A MONO-CAMERA USING RADAR AND MOTION DATA 审中-公开

    公开(公告)号：US20200167941A1

    公开(公告)日：2020-05-28

    申请号：US16200932

    申请日：2018-11-27

    Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

    Inventor： Wei Tong ,  Shuqing Zeng ,  Mohannad Murad ,  Alaa M. Khamis

    IPC: G06T7/55 ,  G06K9/62 ,  G01S13/86 ,  G01S13/93

    Abstract: Systems and methods for depth estimation of images from a mono-camera by use of radar data by: receiving, a plurality of input 2-D images from the mono-camera; generating, by the processing unit, an estimated depth image by supervised training of an image estimation model; generating, by the processing unit, a synthetic image from a first input image and a second input image from the mono-camera by applying an estimated transform pose; comparing, by the processing unit, an estimated three-dimensional (3-D) point cloud to radar data by applying another estimated transform pose to a 3-D point cloud wherein the 3-D point cloud is estimated from a depth image by supervised training of the image estimation model to radar distance and radar doppler measurement; correcting a depth estimation of the estimated depth image by losses derived from differences: of the synthetic image and original images; of an estimated depth image and a measured radar distance; and of an estimated doppler information and measured radar doppler information.

    VEHICLE GLASS CONTAMINATION ASSESSMENT FOR OPTIMIZED AUTO-ACTIVATION OF CLEANING SYSTEM
    27.
    发明申请
    VEHICLE GLASS CONTAMINATION ASSESSMENT FOR OPTIMIZED AUTO-ACTIVATION OF CLEANING SYSTEM 有权

    公开(公告)号：US20250136057A1

    公开(公告)日：2025-05-01

    申请号：US18499362

    申请日：2023-11-01

    Applicant: GM Global Technology Operations LLC

    Inventor： Alaa M. Khamis ,  William Cavalcante Araujo ,  Yun Qian Miao ,  Julien P. Mourou

    IPC: B60S1/48

    Abstract: A vehicle includes a system for cleaning a contaminant from a surface of a vehicle. The system includes a camera for obtaining an image of the surface, the surface including the contaminant, a plurality of cleaning devices for cleaning the contaminant from the surface, and a processor. The processor is configured to determine a contamination measure from the image, the contamination measure indicative of a contamination level of the surface from the image, determine a contaminated region and a contaminant type from the image, select a cleaning approach for cleaning the surface based on the contamination measure, the contaminated region, and the contaminant type, the cleaning approach including selecting a cleaning device from the plurality of cleaning devices, selecting a cleaning direction and selecting a cleaning duration, and control the cleaning device using the cleaning approach.

    FINE-GRAINED IN-VEHICLE DYNAMIC NOISE PATTERN LEARNING FOR VOICE APPLICATIONS
    29.
    发明申请
    FINE-GRAINED IN-VEHICLE DYNAMIC NOISE PATTERN LEARNING FOR VOICE APPLICATIONS 有权

    公开(公告)号：US20250029598A1

    公开(公告)日：2025-01-23

    申请号：US18355685

    申请日：2023-07-20

    Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

    Inventor： Alaa M. Khamis ,  Xu Fang Zhao ,  Gaurav Talwar ,  Kenneth R. Booker

    IPC: G10L15/06 ,  G10L21/0224

    Abstract: Aspects of fine-grained, dynamic noise pattern learning for voice applications include a vehicle, the vehicle having a body with a cabin. Embedded within the vehicle is a processor coupled to memory. The processor may be configured to embed multimodal data for environment and vehicle data. The embedded acoustic data may be from microphone-captured data in the cabin. The processor may concatenate the embeddings to form a latent vector characterizing the embeddings to thereby estimate a mean and variance of the latent vector using an adaptive time window. The processor may identify a noise type using the mean and variance of the latent vector, the noise type identification being fine-grained via the adaptive time window to accurately emulate vehicle noise.

    VOICEPRINT DRIFT DETECTION AND UPDATE
    30.
    发明公开
    VOICEPRINT DRIFT DETECTION AND UPDATE 审中-公开

    公开(公告)号：US20240355332A1

    公开(公告)日：2024-10-24

    申请号：US18303754

    申请日：2023-04-20

    Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

    Inventor： Gaurav Talwar ,  Alaa M. Khamis ,  Xu Fang Zhao ,  Kenneth R. Booker

    IPC: G10L17/04 ,  G10L17/06 ,  G10L17/22

    CPC classification number: G10L17/04 ,  G10L17/06 ,  G10L17/22

    Abstract: A voiceprint module for in-vehicle voiceprint drift detection. The module may be configured for determining an archived voiceprint for an operator of a vehicle, generating a first real-time voiceprint according to in-vehicle utterances of the operator made while operating the vehicle, generating a first voiceprint deviation to quantify statistical distance between one or more probabilistic density functions associated with each of the first real-time and archived voiceprints, and updating one or more vocal characteristics of the archived voiceprint to generate an updated voiceprint in response to the first voiceprint deviation surpassing an update threshold.

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