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    Object classification using extra-regional context
    14.
    发明授权
    Object classification using extra-regional context 有权

    公开(公告)号：US11783568B2

    公开(公告)日：2023-10-10

    申请号：US17224763

    申请日：2021-04-07

    Applicant: Waymo LLC

    Inventor： Junhua Mao ,  Qian Yu ,  Congcong Li

    IPC: G06N3/08 ,  G06V20/58 ,  G06V10/764 ,  G05D1/00 ,  G05D1/02 ,  G06F18/2413 ,  G06V10/82

    CPC classification number: G06V10/764 ,  G05D1/0088 ,  G05D1/0221 ,  G05D1/0231 ,  G06F18/2413 ,  G06N3/08 ,  G06V10/82 ,  G06V20/58 ,  G05D2201/0213

    Abstract: Some aspects of the subject matter disclosed herein include a system implemented on one or more data processing apparatuses. The system can include an interface configured to obtain, from one or more sensor subsystems, sensor data describing an environment of a vehicle, and to generate, using the sensor data, (i) one or more first neural network inputs representing sensor measurements for a particular object in the environment and (ii) a second neural network input representing sensor measurements for at least a portion of the environment that encompasses the particular object and additional portions of the environment that are not represented by the one or more first neural network inputs; and a convolutional neural network configured to process the second neural network input to generate an output, the output including a plurality of feature vectors that each correspond to a different one a plurality of regions of the environment.

    PEDESTRIAN CROSSING INTENT YIELDING
    15.
    发明申请
    PEDESTRIAN CROSSING INTENT YIELDING 有权

    公开(公告)号：US20230062158A1

    公开(公告)日：2023-03-02

    申请号：US17902670

    申请日：2022-09-02

    Applicant: Waymo LLC

    Inventor： Xinwei Shi ,  Junhua Mao ,  Khaled Refaat ,  Tian Lan ,  Jeonhyung Kang ,  Zhishuai Zhang ,  Jonathan Chandler Stroud

    IPC: B60W60/00 ,  B60W40/04 ,  B60W50/00 ,  G06N3/08

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that determine yield behavior for an autonomous vehicle, and can include identifying an agent that is in a vicinity of an autonomous vehicle navigating through a scene at a current time point. Scene features can be obtained and can include features of (i) the agent and (ii) the autonomous vehicle. An input that can include the scene features can be processed using a first machine learning model that is configured to generate (i) a crossing intent prediction that includes a crossing intent score that represents a likelihood that the agent intends to cross a roadway in a future time window after the current time, and (ii) a crossing action prediction that includes a crossing action score that represents a likelihood that the agent will cross the roadway in the future time window after the current time.

    TRAINING A CLASSIFIER TO DETECT OPEN VEHICLE DOORS
    18.
    发明申请
    TRAINING A CLASSIFIER TO DETECT OPEN VEHICLE DOORS 有权

    公开(公告)号：US20230099920A1

    公开(公告)日：2023-03-30

    申请号：US17994991

    申请日：2022-11-28

    Applicant: Waymo LLC

    Inventor： Junhua Mao ,  Lo Po Tsui ,  Congcong Li ,  Edward Stephen Walker, JR.

    IPC: G06N3/08 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a classifier to detect open vehicle doors. One of the methods includes obtaining a plurality of initial training examples, each initial training example comprising (i) a sensor sample from a collection of sensor samples and (ii) data classifying the sensor sample as characterizing a vehicle that has an open door; generating a plurality of additional training examples, comprising, for each initial training example: identifying, from the collection of sensor samples, one or more additional sensor samples that were captured less than a threshold amount of time before the sensor sample in the initial training example was captured; and training the machine learning classifier on first training data that includes the initial training examples and the additional training examples to generate updated weights for the machine learning classifier.

    Neural networks for coarse- and fine-object classifications
    19.
    发明授权
    Neural networks for coarse- and fine-object classifications 有权

    公开(公告)号：US11361187B1

    公开(公告)日：2022-06-14

    申请号：US17118989

    申请日：2020-12-11

    Applicant: Waymo LLC

    Inventor： Junhua Mao ,  Congcong Li ,  Yang Song

    IPC: G06K9/62 ,  G05D1/02 ,  G06N3/04 ,  G06N3/08 ,  G06V20/56

    Abstract: Aspects of the subject matter disclosed herein include methods, systems, and other techniques for training, in a first phase, an object classifier neural network with a first set of training data, the first set of training data including a first plurality of training examples, each training example in the first set of training data being labeled with a coarse-object classification; and training, in a second phase after completion of the first phase, the object classifier neural network with a second set of training data, the second set of training data including a second plurality of training examples, each training example in the second set of training data being labeled with a fine-object classification.

    RARE POSE DATA GENERATION
    20.
    发明申请
    RARE POSE DATA GENERATION 有权

    公开(公告)号：US20220156511A1

    公开(公告)日：2022-05-19

    申请号：US17528129

    申请日：2021-11-16

    Applicant: Waymo LLC

    Inventor： Xiaohan Jin ,  Junhua Mao ,  Ruizhongtai Qi ,  Congcong Li ,  Huayi Zeng

    IPC: G06K9/62 ,  G06K9/00 ,  G06T17/20 ,  G06T15/06 ,  G06T19/20 ,  G06N3/08

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating rare pose data. One of the methods includes obtaining a three-dimensional model of a dynamic object, wherein the dynamic object has multiple movable elements that define a plurality of poses of the dynamic object. A plurality of template poses of the dynamic object are used to generate additional poses for the dynamic object including varying angles of one or more key joints of the dynamic object according to the three-dimensional model. Point cloud data is generated for the additional poses generated for the dynamic object.

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