The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Sep. 03, 2024

Filed:

Nov. 24, 2021
Applicant:

Zoox, Inc., Foster City, CA (US);

Inventors:

Gowtham Garimella, Dublin, CA (US);

Marin Kobilarov, Baltimore, MD (US);

Andres Guillermo Morales Morales, San Francisco, CA (US);

Ethan Miller Pronovost, San Mateo, CA (US);

Kai Zhenyu Wang, Foster City, CA (US);

Xiaosi Zeng, San Mateo, CA (US);

Assignee:

Zoox, Inc., Foster City, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/44 (2022.01); B60W 60/00 (2020.01); G06F 16/901 (2019.01); G06V 10/422 (2022.01); G06V 10/764 (2022.01);
U.S. Cl.
CPC ...
G06V 10/454 (2022.01); B60W 60/0027 (2020.02); G06F 16/9024 (2019.01); G06V 10/422 (2022.01); G06V 10/764 (2022.01);
Abstract

Techniques for determining unified futures of objects in an environment are discussed herein. Techniques may include determining a first feature associated with an object in an environment and a second feature associated with the environment and based on a position of the object in the environment, updating a graph neural network (GNN) to encode the first feature and second feature into a graph node representing the object and encode relative positions of additional objects in the environment into one or more edges attached to the node. The GNN may be decoded to determine a distribution of predicted positions for the object in the future. A predicted position of the object at a subsequent timestep may be determined by sampling from the distribution of predicted positions according to various sampling strategies. Alternatively, the predicted position of the object may be overwritten using a candidate position of the object.


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