Zoox, Inc.

Xianan Huang

Timothy Caldwell

Marin Kobilarov

Joseph Funke

Linjun Zhang

David Evan Zlotnik

Lichao Ma

Matthew Van Heukelom

Rasmus Fonseca

Chonhyon Park

Yunpeng Pan

Arian Houshmand

Dhanushka Nirmevan Kularatne

Joshua Dean Egbert

Ke Sun

Joseph Lorenzetti

Cheng Peng

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

Techniques for accurately predicting vehicle state errors for avoiding collisions with objects detected in an environment of a vehicle are discussed herein. A vehicle safety system can implement a model determine a representation of the vehicle usable in a scenario. The model may dynamically determine a size and/or a heading of the vehicle representation based on differences between a candidate trajectory and a current trajectory of the vehicle. The safety system can identify a potential collision between the vehicle and the object based at least in part on an overlap of the vehicle representation and an object representation.

Vehicle representation determination

Trajectory generation for controlling motion or other behavior of an autonomous vehicle may include alternately determining a candidate action and predicting a future state based on that candidate action. The technique may include determining a cost associated with the candidate action that may include an estimation of a transition cost from a current or former state to a next state of the vehicle. This cost estimate may be a lower bound cost or an upper bound cost and the tree search may alternately apply the lower bound cost or upper bound cost exclusively or according to a ratio or changing ratio. The prediction of the future state may be based at least in part on a machine-learned model's classification of a dynamic object as being a reactive object or a passive object, which may change how the dynamic object is modeled for the prediction.

Vehicle trajectory control using a tree search

Growing community of inventors

Foster City, CA, United States of America

Xianan Huang