PROCESSING SPARSE TOP-DOWN INPUT REPRESENTATIONS OF AN ENVIRONMENT USING NEURAL NETWORKS

    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

    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

    公开(公告)号:US20220301182A1

    公开(公告)日:2022-09-22

    申请号:US17698930

    申请日:2022-03-18

    Applicant: Waymo LLC

    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.

    Neural networks with attention al bottlenecks for trajectory planning

    公开(公告)号:US11565715B2

    公开(公告)日:2023-01-31

    申请号:US17020399

    申请日:2020-09-14

    Applicant: Waymo LLC

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for planning a trajectory of a vehicle. One of the methods includes obtaining input data for planning a driving trajectory for a vehicle, the input data comprising an intended route for the vehicle and data characterizing an environment in a vicinity of the vehicle; processing the input data using an input encoder neural network to generate feature data that includes a respective feature representation for each of a plurality of locations in the environment; applying spatial attention to the feature representations to generate a respective attention weight for each of the plurality of locations; generating a respective attended feature representation for each of the plurality of locations; generating a bottlenecked representation of the attended feature representations; and generating a planned future trajectory from at least the bottlenecked representation.

    Predicting the future movement of agents in an environment using occupancy flow fields

    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

    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.

    NEURAL NETWORKS WITH ATTENTIONAL BOTTLENECKS FOR TRAJECTORY PLANNING

    公开(公告)号:US20210078594A1

    公开(公告)日:2021-03-18

    申请号:US17020399

    申请日:2020-09-14

    Applicant: Waymo LLC

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for planning a trajectory of a vehicle. One of the methods includes obtaining input data for planning a driving trajectory for a vehicle, the input data comprising an intended route for the vehicle and data characterizing an environment in a vicinity of the vehicle; processing the input data using an input encoder neural network to generate feature data that includes a respective feature representation for each of a plurality of locations in the environment; applying spatial attention to the feature representations to generate a respective attention weight for each of the plurality of locations; generating a respective attended feature representation for each of the plurality of locations; generating a bottlenecked representation of the attended feature representations; and generating a planned future trajectory from at least the bottlenecked representation.

Patent Agency Ranking