公开(公告)号：US20230334842A1

公开(公告)日：2023-10-19

申请号：US18136252

申请日：2023-04-18

Applicant: Waymo LLC

Inventor： Alex Zihao Zhu ,  Vincent Michael Casser ,  Henrik Kretzschmar ,  Reza Mahjourian ,  Soeren Pirk

IPC: G06V10/82 ,  G06V10/774

CPC classification number: G06V10/82 ,  G06V10/774

Abstract: Methods, systems, and apparatus for processing inputs that include video frames using neural networks. In one aspect, a system comprises one or more computers configured to obtain a set of one or more training images and, for each training image, ground truth instance data that identifies, for each of one or more object instances, a corresponding region of the training image that depicts the object instance. For each training image in the set, the one or more computers process the training image using an instance segmentation neural network to generate an embedding output comprising a respective embedding for each of a plurality of output pixels. The one or more computers then train the instance segmentation neural network to minimize a loss function.

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.

公开(公告)号：US11926347B2

公开(公告)日：2024-03-12

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 ,  G06N3/045

CPC classification number: B60W60/00272 ,  B60W60/00274 ,  G06N3/045

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.

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

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.

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.

公开(公告)号：US20210390407A1

公开(公告)日：2021-12-16

申请号：US17344254

申请日：2021-06-10

Applicant: Waymo LLC

Inventor： Vincent Michael Casser ,  Yuning Chai ,  Dragomir Anguelov ,  Hang Zhao ,  Henrik Kretzschmar ,  Reza Mahjourian ,  Anelia Angelova ,  Ariel Gordon ,  Soeren Pirk

IPC: G06N3/08 ,  G06K9/62 ,  G06K9/00

Abstract: Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for training a perspective computer vision model. The model is configured to receive input data characterizing an input scene in an environment from an input viewpoint and to process the input data in accordance with a set of model parameters to generate an output perspective representation of the scene from the input viewpoint. The system trains the model based on first data characterizing a scene in the environment from a first viewpoint and second data characterizing the scene in the environment from a second, different viewpoint.