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    AGENT TRAJECTORY PREDICTION USING TARGET LOCATIONS
    4.
    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.

    PROCESSING SPARSE TOP-DOWN INPUT REPRESENTATIONS OF AN ENVIRONMENT USING NEURAL NETWORKS
    7.
    发明申请
    PROCESSING SPARSE TOP-DOWN INPUT REPRESENTATIONS OF AN ENVIRONMENT USING NEURAL NETWORKS 有权

    公开(公告)号：US20220155096A1

    公开(公告)日：2022-05-19

    申请号：US17527676

    申请日：2021-11-16

    Applicant: Waymo LLC

    Inventor： Jinkyu Kim ,  Reza Mahjourian ,  Scott Morgan Ettinger ,  Brandyn Allen White ,  Benjamin Sapp

    IPC: G01C21/00 ,  G06N3/04 ,  G06N3/08

    Abstract: Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for generating a prediction that characterizes an environment. The system obtains an input including data characterizing observed trajectories one or more agents and data characterizing one or more map features identified in a map of the environment. The system generates, from the input, an encoder input that comprises representations for each of a plurality of points in a top-down representation of the environment. The system processes the encoder input using a point cloud encoder neural network to generate a global feature map of the environment, and processes a prediction input including the global feature map using a predictor neural network to generate a prediction output characterizing the environment.

    Predicting the future movement of agents in an environment using occupancy flow fields
    8.
    发明授权
    Predicting the future movement of agents in an environment using occupancy flow fields 有权

    公开(公告)号：US12299898B2

    公开(公告)日：2025-05-13

    申请号：US17698930

    申请日：2022-03-18

    Applicant: Waymo LLC

    Inventor： Reza Mahjourian ,  Jinkyu Kim ,  Yuning Chai ,  Mingxing Tan ,  Benjamin Sapp ,  Dragomir Anguelov

    IPC: G06T7/20 ,  G06V10/40 ,  G06V20/56 ,  G06V20/58

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting the future movement of agents in an environment. In particular, the future movement is predicted through occupancy flow fields that specify, for each future time point in a sequence of future time points and for each agent type in a set of one or more agent types: an occupancy prediction for the future time step that specifies, for each grid cell, an occupancy likelihood that any agent of the agent type will occupy the grid cell at the future time point, and a motion flow prediction that specifies, for each grid cell, a motion vector that represents predicted motion of agents of the agent type within the grid cell at the future time point.

    BEHAVIOR PREDICTION USING SCENE-CENTRIC REPRESENTATIONS
    9.
    发明申请
    BEHAVIOR PREDICTION USING SCENE-CENTRIC REPRESENTATIONS 有权

    公开(公告)号：US20250121857A1

    公开(公告)日：2025-04-17

    申请号：US18913074

    申请日：2024-10-11

    Applicant: Waymo LLC

    Inventor： Bertrand Robert Douillard ,  Aurick Qikun Zhou ,  Rami Al-Rfou ,  Kratarth Goel ,  Benjamin Sapp ,  Andre Liang Cornman ,  Cheolho Park ,  Lingyun Liu

    IPC: B60W60/00 ,  B60W50/00 ,  G06F30/15

    Abstract: A method performed by one or more computers, the method comprising: obtaining scene context data characterizing a scene in an environment at a current time point, wherein the scene context data includes features of the scene in a scene-centric coordinate system; generating a scene-centric encoded representation of the scene in the environment by processing the scene context data using an encoder neural network; for each target agent: obtaining agent-specific features for the target agent, processing the agent-specific features for the target agent and the scene-centric encoded representation of the scene using a fusion neural network to generate a fused scene representation for the target agent, and processing the fused scene representation for the target agent using a decoder neural network to generate a trajectory prediction output for the target agent in an agent-centric coordinate system for the target agent.

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