Zoox, Inc.

Ke Sun

Marin Kobilarov

Linjun Zhang

Gary Linscott

Ethan Miller Pronovost

Timothy Caldwell

Peter Scott Schleede

Yunpeng Pan

Xianan Huang

Joseph Lorenzetti

Xiangyu Xie

Aaron Huang

Yan Chang

A machine-learned architecture for estimating the cost determined by a cost function for a prediction node of a tree search for exploring potential paths for controlling a vehicle may include two portions: a set up portion that includes models trained to process static data and a second portion that processes dynamic object data. The respective portions of the architecture may comprise various models that determine intermediate outputs that may be projected into a space associated with estimated cost. That estimated cost may identify an estimate of an output of the cost function for paths that are based on a particular prediction node of the tree search.

Machine-learned cost estimation in tree search trajectory generation for vehicle control

Techniques for determining a vehicle trajectory that causes a vehicle to navigate in an environment relative to one or more objects are described herein. For example, the techniques may include a computing device determining a decision tree having nodes to represent different object intents and/or nodes to represent vehicle actions at a future time. A tree search algorithm can search the decision tree to evaluate potential interactions between the vehicle and the one or more objects over a time period, and output a vehicle trajectory for the vehicle. The vehicle trajectory can be sent to a vehicle computing device for consideration during vehicle planning, which may include simulation.

Trajectory prediction based on a decision tree

Growing community of inventors

Foster City, CA, United States of America

Ke Sun