Machine learning training platform

    公开(公告)号:US11941519B2

    公开(公告)日:2024-03-26

    申请号:US16699920

    申请日:2019-12-02

    Applicant: Waymo LLC

    CPC classification number: G06N3/08 G06N3/04 G06N3/10

    Abstract: Aspects of the disclosure relate to training a machine learning model on a distributed computing system. The model can be trained using selected processors of the training platform. The distributed system automatically modifies the model for instantiation on each processor, adjusts an input pipeline to accommodate the capabilities of selected processors, and coordinates the training between those processors. Simultaneous processing at each stage can be scaled to reduce or eliminate bottlenecks in the distributed system. In addition, autonomous monitoring and re-allocating of resources can further reduce or eliminate bottlenecks. The training results may be aggregated by the distributed system, and a final model may then be transmitted to a user device.

    TRAFFIC LIGHT DETECTION AND LANE STATE RECOGNITION FOR AUTONOMOUS VEHICLES

    公开(公告)号:US20210343150A1

    公开(公告)日:2021-11-04

    申请号:US17336856

    申请日:2021-06-02

    Applicant: Waymo LLC

    Abstract: Methods and system are provided for training and using a model to determine states of lanes of interest. For instance, image data including an image and an associated label identifying at least one traffic light, a state of the at least one traffic light, and a lane controlled by the at least one traffic light are received and used to train the model such that the model is configured to, in response to receiving an image and a lane of interest included in the image, output a lane state for the lane of interest. This model is then used by a vehicle in order to determine a state of a lane of interest. This state is then used to control the vehicle in an autonomous driving mode based on the state of the lane of interest.

    DETECTING TRAFFIC SIGNALING STATES WITH NEURAL NETWORKS

    公开(公告)号:US20220335731A1

    公开(公告)日:2022-10-20

    申请号:US17738339

    申请日:2022-05-06

    Applicant: Waymo LLC

    Abstract: Machine-learning models are described detecting the signaling state of a traffic signaling unit. A system can obtain an image of the traffic signaling unit, and select a model of the traffic signaling unit that identifies a position of each traffic lighting element on the unit. First and second neural network inputs are processed with a neural network to generate an estimated signaling state of the traffic signaling unit. The first neural network input can represent the image of the traffic signaling unit, and the second neural network input can represent the model of the traffic signaling unit. Using the estimated signaling state of the traffic signaling unit, the system can inform a driving decision of a vehicle.

    DETECTING TRAFFIC SIGNALING STATES WITH NEURAL NETWORKS

    公开(公告)号:US20220027645A1

    公开(公告)日:2022-01-27

    申请号:US16936739

    申请日:2020-07-23

    Applicant: Waymo LLC

    Abstract: Machine-learning models are described detecting the signaling state of a traffic signaling unit. A system can obtain an image of the traffic signaling unit, and select a model of the traffic signaling unit that identifies a position of each traffic lighting element on the unit. First and second neural network inputs are processed with a neural network to generate an estimated signaling state of the traffic signaling unit. The first neural network input can represent the image of the traffic signaling unit, and the second neural network input can represent the model of the traffic signaling unit. Using the estimated signaling state of the traffic signaling unit, the system can inform a driving decision of a vehicle.

    MACHINE LEARNING TRAINING PLATFORM
    10.
    发明申请

    公开(公告)号:US20210166117A1

    公开(公告)日:2021-06-03

    申请号:US16699920

    申请日:2019-12-02

    Applicant: Waymo LLC

    Abstract: Aspects of the disclosure relate to training a machine learning model on a distributed computing system. The model can be trained using selected processors of the training platform. The distributed system automatically modifies the model for instantiation on each processor, adjusts an input pipeline to accommodate the capabilities of selected processors, and coordinates the training between those processors. Simultaneous processing at each stage can be scaled to reduce or eliminate bottlenecks in the distributed system. In addition, autonomous monitoring and re-allocating of resources can further reduce or eliminate bottlenecks. The training results may be aggregated by the distributed system, and a final model may then be transmitted to a user device.

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