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    UNIFICATION OF SPECIALIZED MACHINE-LEARNING MODELS FOR EFFICIENT OBJECT DETECTION AND CLASSIFICATION
    1.
    发明公开
    UNIFICATION OF SPECIALIZED MACHINE-LEARNING MODELS FOR EFFICIENT OBJECT DETECTION AND CLASSIFICATION 审中-公开

    公开(公告)号：US20230351243A1

    公开(公告)日：2023-11-02

    申请号：US17730436

    申请日：2022-04-27

    Applicant: Waymo LLC

    Inventor： Fei Xia ,  Zijian Guo

    IPC: G06N20/00

    CPC classification number: G06N20/00

    Abstract: The described aspects and implementations enable efficient calibration of a sensing system of a vehicle. In one implementation, disclosed is a method and a system to perform the method of obtaining a plurality of target outputs generated by processing a training input using a respective teacher machine learning model (MLM) of a plurality of teacher MLMs. The training input includes a representation of one or more objects, and each of the plurality of target outputs includes a classification of the objects among a respective set of classes of a plurality of sets of classes. The method further includes using the training input and the plurality of target outputs to train a student MLM to classify the one or more objects among each of the plurality of sets of classes.

    TRAINING DISTILLED MACHINE LEARNING MODELS USING A PRE-TRAINED FEATURE EXTRACTOR
    2.
    发明申请
    TRAINING DISTILLED MACHINE LEARNING MODELS USING A PRE-TRAINED FEATURE EXTRACTOR 有权

    公开(公告)号：US20220366263A1

    公开(公告)日：2022-11-17

    申请号：US17313655

    申请日：2021-05-06

    Applicant: Waymo LLC

    Inventor： Ming Ji ,  Edward Stephen Walker, JR. ,  Yang Song ,  Zijian Guo ,  Congcong Li

    IPC: G06N3/08 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a student machine learning model using a teacher machine learning model that has a pre-trained feature extractor. In one aspect, a method includes obtaining data specifying the teacher machine learning model that is configured to perform a machine learning task; obtaining first training data; training the teacher machine learning model on the first training data to obtain a trained teacher machine learning model; generating second, automatically labeled training data by using the trained teacher machine learning model to process unlabeled training data; and training a student machine learning model to perform the machine learning task using at least the second, automatically labeled training data, wherein the student machine learning model does not include the pre-trained feature extractor and instead includes a different feature extractor having fewer parameters than the pre-trained feature extractor.

    SEARCHING AN AUTONOMOUS VEHICLE SENSOR DATA REPOSITORY BASED ON CONTEXT EMBEDDING
    5.
    发明申请
    SEARCHING AN AUTONOMOUS VEHICLE SENSOR DATA REPOSITORY BASED ON CONTEXT EMBEDDING 有权

    公开(公告)号：US20220164350A1

    公开(公告)日：2022-05-26

    申请号：US17104921

    申请日：2020-11-25

    Applicant: Waymo LLC

    Inventor： Jiyang Gao ,  Zijian Guo ,  Congcong Li ,  Xiaowei Li

    IPC: G06F16/2458 ,  G06F16/2455 ,  G06F16/248 ,  G06K9/00 ,  G06N3/08 ,  B60W60/00

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for searching an autonomous vehicle sensor data repository. One of the methods includes maintaining a collection of sensor samples and one or more embeddings of each sensor sample. Each sensor sample is generated from sensor data at multiple time steps and characterizes an environment at each of the multiple time steps. Each embedding corresponds to a respective portion of the sensor sample and has been generated by an embedding neural network. A query specifying a query portion of a query sensor sample is received. A query embedding corresponding to the query portion of the query sensor sample is generated through the embedding neural network. A plurality of relevant sensor samples that have embeddings that are closest to the query embedding are identified as characterizing similar scenarios to the query portion of the query sensor sample.

    Spatio-temporal embeddings
    6.
    发明授权
    Spatio-temporal embeddings 有权

    公开(公告)号：US11657291B2

    公开(公告)日：2023-05-23

    申请号：US17063553

    申请日：2020-10-05

    Applicant: Waymo LLC

    Inventor： Jiyang Gao ,  Zijian Guo ,  Congcong Li

    IPC: G06V20/58 ,  G06N3/08 ,  G06N3/04 ,  G06V10/75

    CPC classification number: G06V20/58 ,  G06N3/0454 ,  G06N3/08 ,  G06V10/757

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a spatio-temporal embedding of a sequence of point clouds. One of the methods includes obtaining a temporal sequence comprising a respective point cloud input corresponding to each of a plurality of time points, each point cloud input comprising point cloud data generated from sensor data captured by one or more sensors of a vehicle at the respective time point; processing each point cloud input using a first neural network to generate a respective spatial embedding that characterizes the point cloud input; and processing the spatial embeddings of the point cloud inputs using a second neural network to generate a spatio-temporal embedding that characterizes the point cloud inputs in the temporal sequence.

    LEARNING POINT CLOUD AUGMENTATION POLICIES
    7.
    发明申请
    LEARNING POINT CLOUD AUGMENTATION POLICIES 有权

    公开(公告)号：US20210284184A1

    公开(公告)日：2021-09-16

    申请号：US17194072

    申请日：2021-03-05

    Applicant: Waymo LLC

    Inventor： Yang Song ,  Shuyang Cheng ,  Zijian Guo ,  Congcong Li ,  Chunyan Bai

    IPC: B60W50/06 ,  G06N3/04 ,  G06N3/08 ,  B60W60/00

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a point cloud augmentation policy and training a machine learning model using the point cloud augmentation policy to perform a perception task such as object detection or classification task by processing point cloud data. In general, training a machine learning model using the determined point cloud augmentation policy enables the model to more effectively perform the perception task, i.e., by generating higher quality perception outputs. When deployed within an on-board system of a vehicle, the machine learning model can further enable the on-board system to generate better-informed planning decisions which in turn result in a safer journey, even when the vehicle is navigating through unconventional environments or inclement weathers such as rain or snow.

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