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    CONDITIONAL AGENT TRAJECTORY PREDICTION
    11.
    发明申请
    CONDITIONAL AGENT TRAJECTORY PREDICTION 有权

    公开(公告)号：US20220135086A1

    公开(公告)日：2022-05-05

    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 ,  G06N3/04

    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.

    Efficient three-dimensional object detection from point clouds
    15.
    发明授权
    Efficient three-dimensional object detection from point clouds 有权

    公开(公告)号：US12125298B2

    公开(公告)日：2024-10-22

    申请号：US17527653

    申请日：2021-11-16

    Applicant: Waymo LLC

    Inventor： Pei Sun ,  Weiyue Wang ,  Yuning Chai ,  Xiao Zhang ,  Dragomir Anguelov

    IPC: G06V20/64 ,  G06T7/194

    CPC classification number: G06V20/64 ,  G06T7/194 ,  G06T2207/10028 ,  G06T2207/20081 ,  G06T2207/20084

    Abstract: Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for performing object detection. The system obtains a respective range image corresponding to each point cloud in a set of point clouds captured by one or more sensors. The system processes each range image using a segmentation neural network to generate range image features and a segmentation output. The system generates a feature representation of the set of point clouds from only the feature representations of the foreground points. The system processes the feature representation of the set of point clouds using a prediction neural network to generate a prediction characterizing the set of point clouds.

    Object-centric three-dimensional auto labeling of point cloud data
    16.
    发明授权
    Object-centric three-dimensional auto labeling of point cloud data 有权

    公开(公告)号：US12073575B2

    公开(公告)日：2024-08-27

    申请号：US17407795

    申请日：2021-08-20

    Applicant: Waymo LLC

    Inventor： Ruizhongtai Qi ,  Yin Zhou ,  Dragomir Anguelov ,  Pei Sun

    IPC: G06T7/521 ,  G06T7/20

    CPC classification number: G06T7/521 ,  G06T7/20 ,  G06T2207/10028 ,  G06T2207/20084

    Abstract: Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for performing three-dimensional auto-labeling on sensor data. The system obtains a sensor data segment that includes a temporal sequence of three-dimensional point clouds generated from sensor readings of an environment by one or more sensors. The system identifies, from the sensor data segment, (i) a plurality of object tracks that each corresponds to a different object in the environment and (ii) for each object track, respective initial three-dimensional regions in each of one or more of the point clouds in which the corresponding object appears. The system generates, for each object track, extracted object track data that includes at least the points in the respective initial three-dimensional regions for the object track. The system further generates, for each object track and from the extracted object track data for the object track, an auto labeling output that defines respective refined three-dimensional regions in each of the one or more point clouds.

    AGENT TRAJECTORY PREDICTION USING TARGET LOCATIONS
    17.
    发明公开
    AGENT TRAJECTORY PREDICTION USING TARGET LOCATIONS 审中-公开

    公开(公告)号：US20240278803A1

    公开(公告)日：2024-08-22

    申请号：US18423136

    申请日：2024-01-25

    Applicant: Waymo LLC

    Inventor： Hang Zhao ,  Jiyang Gao ,  Chen Sun ,  Yi Shen ,  Yuning Chai ,  Cordelia Luise Schmid ,  Congcong Li ,  Benjamin Sapp ,  Dragomir Anguelov ,  Tian Lan ,  Yue Shen

    IPC: B60W60/00 ,  G06N3/02

    CPC classification number: B60W60/001 ,  G06N3/02 ,  B60W2420/403 ,  B60W2554/4049

    Abstract: Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for predicting future trajectories for an agent in an environment. A system obtains scene context data characterizing the environment. The scene context data includes data that characterizes a trajectory of an agent in a vicinity of a vehicle in an environment up to a current time point. The system identifies a plurality of initial target locations in the environment. The system further generates, for each of a plurality of target locations that each corresponds to one of the initial target locations, a respective predicted likelihood score that represents a likelihood that the target location will be an intended final location for a future trajectory of the agent starting from the current time point. For each target location in a first subset of the target locations, the system generates a predicted future trajectory for the agent that is a prediction of the future trajectory of the agent given that the target location is the intended final location for the future trajectory. The system further selects, as likely future trajectories of the agent starting from the current time point, one or more of the predicted future trajectories.

    BEHAVIOR PREDICTION OF SURROUNDING AGENTS
    18.
    发明公开
    BEHAVIOR PREDICTION OF SURROUNDING AGENTS 审中-公开

    公开(公告)号：US20230343107A1

    公开(公告)日：2023-10-26

    申请号：US18216488

    申请日：2023-06-29

    Applicant: Waymo LLC

    Inventor： Mayank Bansal ,  Dragomir Anguelov

    IPC: G06V20/58 ,  B60W60/00 ,  G06V20/10 ,  G06N3/045 ,  G06N3/047 ,  G06V10/764 ,  G06V10/82

    CPC classification number: G06V20/58 ,  B60W60/00274 ,  B60W60/00259 ,  G06V20/10 ,  G06N3/045 ,  G06N3/047 ,  G06V10/764 ,  G06V10/82

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting occupancies of agents. One of the methods includes obtaining scene data characterizing a current scene in an environment; and processing a neural network input comprising the scene data using a neural network to generate a neural network output, wherein: the neural network output comprises respective occupancy outputs corresponding to a plurality of agent types at one or more future time points; the occupancy output for each agent type at a first future time point comprises respective occupancy probabilities for a plurality of locations in the environment; and in the occupancy output for each agent type at the first future time point, the respective occupancy probability for each location characterizes a likelihood that an agent of the agent type will occupy the location at the first future time point.

    ANNOTATED SURFEL MAPS
    20.
    发明申请
    ANNOTATED SURFEL MAPS 有权

    公开(公告)号：US20230076479A1

    公开(公告)日：2023-03-09

    申请号：US17819883

    申请日：2022-08-15

    Applicant: Waymo LLC

    Inventor： Dragomir Anguelov ,  Colin Andrew Braley ,  Christoph Sprunk

    IPC: G06V20/56 ,  G06K9/62

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for the generation and use of a surfel map with semantic labels. One of the methods includes receiving a surfel map that includes a plurality of surfels, wherein each surfel has associated data that includes one or more semantic labels; obtaining sensor data for one or more locations in the environment, the sensor data having been captured by one or more sensors of a first vehicle; determining one or more surfels corresponding to the one or more locations of the obtained sensor data; identifying one or more semantic labels for the one or more surfels corresponding to the one or more locations of the obtained sensor data; and performing, for each surfel corresponding to the one or more locations of the obtained sensor data, a label-specific detection process for the surfel.

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