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18 条记录 (耗时 0.016 秒)

    MACHINE LEARNING MODELS OPERATING AT DIFFERENT FREQUENCIES FOR AUTONOMOUS VEHICLES
    2.
    发明公开
    MACHINE LEARNING MODELS OPERATING AT DIFFERENT FREQUENCIES FOR AUTONOMOUS VEHICLES 审中-公开

    公开(公告)号：US20240112051A1

    公开(公告)日：2024-04-04

    申请号：US18482332

    申请日：2023-10-06

    申请人： Tesla, Inc.

    发明人： Anting Shen

    IPC分类号： G06N5/04 ,  G06N20/00

    CPC分类号： G06N5/04 ,  G06N20/00

    摘要： Systems and methods include machine learning models operating at different frequencies. An example method includes obtaining images at a threshold frequency from one or more image sensors positioned about a vehicle. Location information associated with objects classified in the images is determined based on the images. The images are analyzed via a first machine learning model at the threshold frequency. For a subset of the images, the first machine learning model uses output information from a second machine learning model, the second machine learning model being performed at less than the threshold frequency.

    ANNOTATION CROSS-LABELING FOR AUTONOMOUS CONTROL SYSTEMS
    3.
    发明申请
    ANNOTATION CROSS-LABELING FOR AUTONOMOUS CONTROL SYSTEMS 有权

    公开(公告)号：US20220375208A1

    公开(公告)日：2022-11-24

    申请号：US17806358

    申请日：2022-06-10

    申请人： Tesla, Inc.

    发明人： Anting Shen

    IPC分类号： G06V10/80 ,  G01S17/894 ,  G01S17/86

    摘要： An annotation system uses annotations for a first set of sensor measurements from a first sensor to identify annotations for a second set of sensor measurements from a second sensor. The annotation system identifies reference annotations in the first set of sensor measurements that indicates a location of a characteristic object in the two-dimensional space. The annotation system determines a spatial region in the three-dimensional space of the second set of sensor measurements that corresponds to a portion of the scene represented in the annotation of the first set of sensor measurements. The annotation system determines annotations within the spatial region of the second set of sensor measurements that indicates a location of the characteristic object in the three-dimensional space.

    Data synthesis for autonomous control systems
    8.
    发明授权
    Data synthesis for autonomous control systems 有权

    公开(公告)号：US10678244B2

    公开(公告)日：2020-06-09

    申请号：US15934899

    申请日：2018-03-23

    申请人： Tesla, Inc.

    发明人： Forrest Nelson Iandola ,  Donald Benton MacMillen ,  Anting Shen ,  Harsimran Singh Sidhu ,  Paras Jagdish Jain

    IPC分类号： G05D1/00 ,  G06K9/62 ,  B60R11/04 ,  B60W40/02 ,  G06K9/00 ,  G06F30/20 ,  G06F30/15

    摘要： An autonomous control system generates synthetic data that reflect simulated environments. Specifically, the synthetic data is a representation of sensor data of the simulated environment from the perspective of one or more sensors. The system generates synthetic data by introducing one or more simulated modifications to sensor data captured by the sensors or by simulating the sensor data for a virtual environment. The autonomous control system uses the synthetic data to train computer models for various detection and control algorithms. In general, this allows autonomous control systems to augment training data to improve performance of computer models, simulate scenarios that are not included in existing training data, and/or train computer models that remove unwanted effects or occlusions from sensor data of the environment.

    Machine learning models operating at different frequencies for autonomous vehicles
    10.
    发明授权
    Machine learning models operating at different frequencies for autonomous vehicles 有权

    公开(公告)号：US11816585B2

    公开(公告)日：2023-11-14

    申请号：US16701669

    申请日：2019-12-03

    申请人： Tesla, Inc.

    发明人： Anting Shen

    IPC分类号： G06N20/10 ,  G06N20/00 ,  G06N5/04

    CPC分类号： G06N5/04 ,  G06N20/00

    摘要： Systems and methods include machine learning models operating at different frequencies. An example method includes obtaining images at a threshold frequency from one or more image sensors positioned about a vehicle. Location information associated with objects classified in the images is determined based on the images. The images are analyzed via a first machine learning model at the threshold frequency. For a subset of the images, the first machine learning model uses output information from a second machine learning model, the second machine learning model being performed at less than the threshold frequency.