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    Machine learning training platform
    112.
    发明授权
    Machine learning training platform 有权

    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

    Inventor： Pok Man Chu ,  Edward Hsiao

    IPC: G06N3/08 ,  G06N3/04 ,  G06N3/10

    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.

    CARRIER EXTRACTION FROM SEMICONDUCTING WAVEGUIDES IN HIGH-POWER LIDAR APPLICATIONS
    113.
    发明公开
    CARRIER EXTRACTION FROM SEMICONDUCTING WAVEGUIDES IN HIGH-POWER LIDAR APPLICATIONS 审中-公开

    公开(公告)号：US20240094354A1

    公开(公告)日：2024-03-21

    申请号：US17947976

    申请日：2022-09-19

    Applicant: Waymo LLC

    Inventor： Alexander Yukio Piggott

    IPC: G01S7/481 ,  G02B6/35

    CPC classification number: G01S7/4818 ,  G02B6/3596

    Abstract: The subject matter of this specification can be implemented in, among other things, systems and methods of optical sensing that use carrier extraction from waveguides that can support propagation of high-power sensing beams. Described, among other things, is a system that includes one or more waveguides that include a semiconducting material with a temperature-dependent refractive index. The system further includes a plurality of extraction electrodes configured to extract from the waveguide(s), charge carriers generated by an electromagnetic wave propagating in the waveguide(s). The system further includes a heating electrode configured to cause a change of a temperature of the waveguide(s).

    Low-Overhead, Bidirectional Error Checking for a Serial Peripheral Interface
    114.
    发明公开
    Low-Overhead, Bidirectional Error Checking for a Serial Peripheral Interface 审中-公开

    公开(公告)号：US20240086278A1

    公开(公告)日：2024-03-14

    申请号：US18512754

    申请日：2023-11-17

    Applicant: Waymo LLC

    Inventor： Kaushik Kannan ,  David Sobel

    IPC: G06F11/10 ,  G06F11/07 ,  G06F11/30 ,  G06F13/42

    CPC classification number: G06F11/1004 ,  G06F11/0772 ,  G06F11/3031 ,  G06F13/4291

    Abstract: Example embodiments relate to low-overhead, bidirectional error checking for a serial peripheral interface. An example device includes an integrated circuit. The device also includes a serial peripheral interface (SPI) with a Master In Slave Out (MISO) channel and a Master Out Slave In (MOSI) channel. The MOSI channel is configured to receive a write address, payload data, and a forward error-checking code usable to identify data corruption within the write address or the payload data. The integrated circuit is configured to calculate and provide a reverse error-checking code usable to identify data corruption within the write address or the payload data. Additionally, the integrated circuit is configured to compare the forward error-checking code to the reverse error-checking code. Further, the integrated circuit is configured to write, to the write address if the forward error-checking code matches the reverse error-checking code, the payload data.

    Sensor assembly
    115.
    外观设计
    Sensor assembly 有权

    公开(公告)号：USD1017436S1

    公开(公告)日：2024-03-12

    申请号：US29818904

    申请日：2021-12-10

    Applicant: Waymo LLC

    Designer： YooJung Ahn ,  Jerry Chen ,  Toshihiro Fujimura ,  Jinseok Hwang ,  Joshua Newby ,  Zhaokun Wang

    Abstract: FIG. 1 is a top, front, left perspective view of a first embodiment of the claimed design for a sensor assembly;
    FIG. 2 is a front view thereof;
    FIG. 3 is a back view thereof;
    FIG. 4 is a right side view thereof;
    FIG. 5 is a left side view thereof;
    FIG. 6 is a top view thereof;
    FIG. 7 is a bottom view thereof;
    FIG. 8 is a top, rear, right perspective view thereof;
    FIG. 9 is a top, front, left perspective view of a second embodiment of the claimed design for a sensor assembly;
    FIG. 10 is a front view thereof;
    FIG. 11 is a back view thereof;
    FIG. 12 is a right side view thereof;
    FIG. 13 is a left side view thereof;
    FIG. 14 is a top view thereof;
    FIG. 15 is a bottom view thereof;
    FIG. 16 is a top, rear, right perspective view thereof;
    FIG. 17 is a top, front, left perspective view of a third embodiment of the claimed design for a sensor assembly;
    FIG. 18 is a front view thereof;
    FIG. 19 is a back view thereof;
    FIG. 20 is a right side view thereof;
    FIG. 21 is a left side view thereof;
    FIG. 22 is a top view thereof;
    FIG. 23 is a bottom view thereof; and,
    FIG. 24 is a top, rear, right perspective view thereof.
    The broken lines showing the remainder of the sensor assembly depict environmental structure and form no part of the claimed design.

    Conditional agent trajectory prediction
    116.
    发明授权
    Conditional agent trajectory prediction 有权

    公开(公告)号：US11926347B2

    公开(公告)日：2024-03-12

    申请号：US17514259

    申请日：2021-10-29

    Applicant: Waymo LLC

    Inventor： Reza Mahjourian ,  Carlton Macdonald Downey ,  Benjamin Sapp ,  Dragomir Anguelov ,  Ekaterina Igorevna Tolstaya

    IPC: B60W60/00 ,  G06N3/045

    CPC classification number: B60W60/00272 ,  B60W60/00274 ,  G06N3/045

    Abstract: Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for performing a conditional behavior prediction for one or more agents. The system obtains context data characterizing an environment. The context data includes data characterizing a plurality of agents, including a query agent and one or more target agents, in the environment at a current time point. The system further obtains data identifying a planned future trajectory for the query agent after the current time point, and for each target agent in the set, processes the context data and the data identifying the planned future trajectory using a first neural network to generate a conditional trajectory prediction output that defines a conditional probability distribution over possible future trajectories of the target agent after the current time point given that the query agent follows the planned future trajectory for the query agent after the current time point.

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