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    THREE-DIMENSIONAL LOCATION PREDICTION FROM IMAGES
    11.
    发明申请
    THREE-DIMENSIONAL LOCATION PREDICTION FROM IMAGES 有权

    公开(公告)号：US20220180549A1

    公开(公告)日：2022-06-09

    申请号：US17545987

    申请日：2021-12-08

    Applicant: Waymo LLC

    Inventor： Longlong Jing ,  Ruichi Yu ,  Jiyang Gao ,  Henrik Kretzschmar ,  Kang Li ,  Ruizhongtai Qi ,  Hang Zhao ,  Alper Ayvaci ,  Xu Chen ,  Dillon Cower ,  Congcong Li

    IPC: G06T7/70 ,  G06V10/40 ,  G06V10/80 ,  G06T7/50 ,  G06N20/00

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting three-dimensional object locations from images. One of the methods includes obtaining a sequence of images that comprises, at each of a plurality of time steps, a respective image that was captured by a camera at the time step; generating, for each image in the sequence, respective pseudo-lidar features of a respective pseudo-lidar representation of a region in the image that has been determined to depict a first object; generating, for a particular image at a particular time step in the sequence, image patch features of the region in the particular image that has been determined to depict the first object; and generating, from the respective pseudo-lidar features and the image patch features, a prediction that characterizes a location of the first object in a three-dimensional coordinate system at the particular time step in the sequence.

    Object action classification for autonomous vehicles
    12.
    发明授权
    Object action classification for autonomous vehicles 有权

    公开(公告)号：US11061406B2

    公开(公告)日：2021-07-13

    申请号：US16167007

    申请日：2018-10-22

    Applicant: Waymo LLC

    Inventor： Junhua Mao ,  Congcong Li ,  Alper Ayvaci ,  Chen Sun ,  Kevin Murphy ,  Ruichi Yu

    IPC: G05D1/02 ,  B60W30/095 ,  G01S17/93 ,  G05D1/00 ,  G06K9/00 ,  G06K9/62 ,  G01S17/931

    Abstract: Aspects of the disclosure relate to training and using a model for identifying actions of objects. For instance, LIDAR sensor data frames including an object bounding box corresponding to an object as well as an action label for the bounding box may be received. Each sensor frame is associated with a timestamp and is sequenced with respect to other sensor frames. Each given sensor data frame may be projected into a camera image of the object based on the timestamp associated with the given sensor data frame in order to provide fused data. The model may be trained using the fused data such that the model is configured to, in response to receiving fused data, the model outputs an action label for each object bounding box of the fused data. This output may then be used to control a vehicle in an autonomous driving mode.

    AUTOMATIC LABELING OF OBJECTS IN SENSOR DATA
    13.
    发明申请
    AUTOMATIC LABELING OF OBJECTS IN SENSOR DATA 有权

    公开(公告)号：US20250103844A1

    公开(公告)日：2025-03-27

    申请号：US18973983

    申请日：2024-12-09

    Applicant: Waymo LLC

    Inventor： Justin Thorsen ,  Changchang Wu ,  Alper Ayvaci ,  Tiffany Chen ,  Lo Po Tsui ,  Zhinan Xu ,  Chen Wu ,  Sean Rafferty

    IPC: G06K19/067 ,  G09F3/00

    Abstract: Aspects of the disclosure provide for automatically generating labels for sensor data. For instance, first sensor data for a vehicle may be identified. This first sensor data may have been captured by a first sensor of the vehicle at a first location during a first point in time and may be associated with a first label for an object. Second sensor data for the vehicle may be identified. The second sensor data may have been captured by a second sensor of the vehicle at a second location at a second point in time outside of the first point in time. The second location is different from the first location. A determination may be made as to whether the object is a static object. Based on the determination that the object is a static object, the first label may be used to automatically generate a second label for the second sensor data.

    Contrastive learning for object detection
    16.
    发明授权
    Contrastive learning for object detection 有权

    公开(公告)号：US11756309B2

    公开(公告)日：2023-09-12

    申请号：US17148148

    申请日：2021-01-13

    Applicant: Waymo LLC

    Inventor： Alper Ayvaci ,  Feiyu Chen ,  Justin Yu Zheng ,  Bayram Safa Cicek ,  Vasiliy Igorevich Karasev

    IPC: G06N3/08 ,  G06V20/58 ,  B60W60/00

    CPC classification number: G06V20/58 ,  B60W60/001 ,  G06N3/08 ,  B60W2420/52 ,  B60W2554/4049

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network using contrastive learning. One of the methods includes obtaining a network input representing an environment; processing the network input using a first subnetwork of the neural network to generate a respective embedding for each location in the environment; processing the embeddings for each location in the environment using a second subnetwork of the neural network to generate a respective object prediction for each location; determining, for each of a plurality of pairs of the plurality of locations in the environment, whether the respective object predictions of the pair of locations characterize the same possible object or different possible objects; computing a respective contrastive loss value for each of the plurality of pairs of locations; and updating values for a plurality of parameters of the first subnetwork using the computed contrastive loss values.

    Interacted Object Detection Neural Network
    19.
    发明申请
    Interacted Object Detection Neural Network 有权

    公开(公告)号：US20210295555A1

    公开(公告)日：2021-09-23

    申请号：US17342434

    申请日：2021-06-08

    Applicant: Waymo LLC

    Inventor： Alper Ayvaci ,  Yu-Han Chen ,  Ruichi Yu ,  Chen Wu ,  Noha Waheed Ahmed Radwan ,  Jonathon Shlens

    IPC: G06T7/73 ,  G06T7/50 ,  G06K9/62 ,  G06N3/08 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating object interaction predictions using a neural network. One of the methods includes obtaining a sensor input derived from data generated by one or more sensors that characterizes a scene. The sensor input is provided to an object interaction neural network. The object interaction neural network is configured to process the sensor input to generate a plurality of object interaction outputs. Each respective object interaction output includes main object information and interacting object information. The respective object interaction outputs corresponding to the plurality of regions in the sensor input are received as output of the object interaction neural network.

    Interacted Object Detection Neural Network
    20.
    发明申请
    Interacted Object Detection Neural Network 有权

    公开(公告)号：US20210150752A1

    公开(公告)日：2021-05-20

    申请号：US16686840

    申请日：2019-11-18

    Applicant: Waymo LLC

    Inventor： Alper Ayvaci ,  Yu-Han Chen ,  Ruichi Yu ,  Chen Wu ,  Noha Waheed Ahmed Radwan ,  Jonathon Shlens

    IPC: G06T7/73 ,  G06T7/50 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating object interaction predictions using a neural network. One of the methods includes obtaining a sensor input derived from data generated by one or more sensors that characterizes a scene. The sensor input is provided to an object interaction neural network. The object interaction neural network is configured to process the sensor input to generate a plurality of object interaction outputs. Each respective object interaction output includes main object information and interacting object information. The respective object interaction outputs corresponding to the plurality of regions in the sensor input are received as output of the object interaction neural network.

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