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    AGENT TRAJECTORY PREDICTION USING TARGET LOCATIONS
    2.
    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.

    THREE-DIMENSIONAL LOCATION PREDICTION FROM IMAGES
    3.
    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.

    SEARCHING AN AUTONOMOUS VEHICLE SENSOR DATA REPOSITORY BASED ON CONTEXT EMBEDDING
    4.
    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.

    Agent trajectory prediction using target locations
    6.
    发明授权
    Agent trajectory prediction using target locations 有权

    公开(公告)号：US11987265B1

    公开(公告)日：2024-05-21

    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: 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 up to a current time point. The system identifies a plurality of initial target locations, and 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. For each target location in a first subset of the target locations, the system generates a predicted future trajectory for 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, one or more of the predicted future trajectories.

    Vehicle Intent Prediction Neural Network
    8.
    发明申请
    Vehicle Intent Prediction Neural Network 有权

    公开(公告)号：US20210191395A1

    公开(公告)日：2021-06-24

    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: G05D1/00 ,  G06K9/00 ,  G06K9/62 ,  G05D1/02 ,  B60W60/00

    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.

    Three-dimensional location prediction from images
    9.
    发明授权
    Three-dimensional location prediction from images 有权

    公开(公告)号：US12299916B2

    公开(公告)日：2025-05-13

    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: G06T7/50 ,  G06T7/70 ,  G06V10/40 ,  G06V10/80 ,  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.

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