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:
Apr. 25, 2023

Filed:

Mar. 12, 2020
Applicant:

Uatc, Llc, San Francisco, CA (US);

Inventors:

Raquel Urtasun, Toronto, CA;

Renjie Liao, Toronto, CA;

Sergio Casas, Toronto, CA;

Cole Christian Gulino, Pittsburgh, PA (US);

Assignee:

UATC, LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/04 (2006.01); G06N 3/08 (2006.01); G08G 1/00 (2006.01); G01C 21/36 (2006.01); G08G 1/01 (2006.01); G06N 7/00 (2006.01); G06N 7/04 (2006.01); G06N 3/06 (2006.01); G06N 3/084 (2023.01);
U.S. Cl.
CPC ...
G06N 3/04 (2013.01); G01C 21/3626 (2013.01); G06N 3/0454 (2013.01); G06N 3/06 (2013.01); G06N 3/08 (2013.01); G06N 3/084 (2013.01); G06N 7/005 (2013.01); G06N 7/046 (2013.01); G08G 1/0133 (2013.01); G08G 1/0141 (2013.01); G08G 1/20 (2013.01);
Abstract

Systems and methods for generating motion forecast data for actors with respect to an autonomous vehicle and training a machine learned model for the same are disclosed. The computing system can include an object detection model and a graph neural network including a plurality of nodes and a plurality of edges. The computing system can be configured to input sensor data into the object detection model; receive object detection data describing the location of the plurality of the actors relative to the autonomous vehicle as an output of the object detection model; input the object detection data into the graph neural network; iteratively update a plurality of node states respectively associated with the plurality of nodes; and receive, as an output of the graph neural network, the motion forecast data with respect to the plurality of actors.


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