Waymo LLC

Zhishuai Zhang

Junhua Mao

Khaled Refaat

Xinwei Shi

Jeonhyung Kang

Dragomir Dimitrov Anguelov

Congcong Li

Jiyang Gao

Tian Lan

Jiachen Li

Jonathan Chandler Stroud

Yukai Liu

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for agent behavior prediction using keypoint data. One of the methods includes obtaining data characterizing a scene in an environment, the data comprising: (i) context data comprising data characterizing historical trajectories of a plurality of agents up to the current time point; and (ii) keypoint data for a target agent; processing the context data using a context data encoder neural network to generate a context embedding for the target agent; processing the keypoint data using a keypoint encoder neural network to generate a keypoint embedding for the target agent; generating a combined embedding for the target agent from the context embedding and the keypoint embedding; and processing the combined embedding using a decoder neural network to generate a behavior prediction output for the target agent that characterizes predicted behavior of the target agent after the current time point.

Pedestrian behavior prediction with 3D human keypoints

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that determine yield behavior for an autonomous vehicle, and can include identifying an agent that is in a vicinity of an autonomous vehicle navigating through a scene at a current time point. Scene features can be obtained and can include features of (i) the agent and (ii) the autonomous vehicle. An input that can include the scene features can be processed using a first machine learning model that is configured to generate (i) a crossing intent prediction that includes a crossing intent score that represents a likelihood that the agent intends to cross a roadway in a future time window after the current time, and (ii) a crossing action prediction that includes a crossing action score that represents a likelihood that the agent will cross the roadway in the future time window after the current time.

Pedestrian crossing intent yielding

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing point cloud data using spatio-temporal-interactive networks.

Spatio-temporal-interactive networks

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Baltimore, MD, United States of America

Zhishuai Zhang