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    SPATIO-TEMPORAL EMBEDDINGS
    15.
    发明申请
    SPATIO-TEMPORAL EMBEDDINGS 有权

    公开(公告)号：US20210103744A1

    公开(公告)日：2021-04-08

    申请号：US17063553

    申请日：2020-10-05

    Applicant: Waymo LLC

    Inventor： Jiyang Gao ,  Zijian Guo ,  Congcong Li

    IPC: G06K9/00 ,  G06N3/04 ,  G06K9/62 ,  G06N3/08

    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.

    AGENT TRAJECTORY PREDICTION USING TARGET LOCATIONS
    16.
    发明公开
    AGENT TRAJECTORY PREDICTION USING TARGET LOCATIONS 审中-公开

    公开(公告)号：US20240149906A1

    公开(公告)日：2024-05-09

    申请号：US17387852

    申请日：2021-07-28

    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 ,  B60W2420/42 ,  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.

    Spatio-temporal embeddings
    17.
    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.

    IMPLEMENTING SYNTHETIC SCENES FOR AUTONOMOUS VEHICLES
    18.
    发明申请
    IMPLEMENTING SYNTHETIC SCENES FOR AUTONOMOUS VEHICLES 有权

    公开(公告)号：US20220402520A1

    公开(公告)日：2022-12-22

    申请号：US17349489

    申请日：2021-06-16

    Applicant: Waymo LLC

    Inventor： Congrui Hetang ,  Yi Shen ,  Youjie Zhou ,  Jiyang Gao

    IPC: B60W60/00 ,  G06N20/00 ,  G01C21/34

    Abstract: A system includes a memory device, and a processing device, operatively coupled to the memory device, to receive a set of input data including a roadgraph, the roadgraph including an autonomous vehicle driving path, modify the roadgraph to obtain a modified roadgraph by adjusting a trajectory of the autonomous vehicle driving path, place a set of artifacts along one or more lane boundaries of the modified roadgraph to generate a synthetic scene, and train a machine learning model used to navigate an autonomous vehicle based on the synthetic scene.

    Vehicle intent prediction neural network
    19.
    发明授权
    Vehicle intent prediction neural network 有权

    公开(公告)号：US11480963B2

    公开(公告)日：2022-10-25

    申请号：US16723787

    申请日：2019-12-20

    Applicant: Waymo LLC

    Inventor： Jiyang Gao ,  Junhua Mao ,  Yi Shen ,  Congcong Li ,  Chen Sun

    IPC: G01C22/00 ,  G05D1/00 ,  G06K9/62 ,  G05D1/02 ,  B60W60/00 ,  G06V20/58 ,  G06V20/56

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating vehicle intent predictions using a neural network. One of the methods includes obtaining an input characterizing one or more vehicles in an environment; generating, from the input, features of each of the vehicles; and for each of the vehicles: processing the features of the vehicle using each of a plurality of intent-specific neural networks, wherein each of the intent-specific neural networks corresponds to a respective intent from a set of intents, and wherein each intent-specific neural network is configured to process the features of the vehicle to generate an output for the corresponding intent.

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