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:
Nov. 12, 2024

Filed:

Jul. 29, 2021
Applicant:

Aurora Operations, Inc., Pittsburgh, PA (US);

Inventors:

Ming Liang, Toronto, CA;

Wei-Chiu Ma, Toronto, CA;

Sivabalan Manivasagam, Toronto, CA;

Raquel Urtasun, Toronto, CA;

Bin Yang, Toronto, CA;

Ze Yang, Toronto, CA;

Assignee:

AURORA OPERATIONS, INC., Pittsburgh, PA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06N 20/00 (2019.01); G06T 7/70 (2017.01); G06T 17/20 (2006.01); G06V 40/20 (2022.01);
U.S. Cl.
CPC ...
G06T 7/70 (2017.01); G06N 20/00 (2019.01); G06T 17/20 (2013.01); G06V 40/23 (2022.01);
Abstract

Systems and methods for generating simulation data based on real-world dynamic objects are provided. A method includes obtaining two- and three-dimensional data descriptive of a dynamic object in the real world. The two- and three-dimensional information can be provided as an input to a machine-learned model to receive object model parameters descriptive of a pose and shape modification with respect to a three-dimensional template object model. The parameters can represent a three-dimensional dynamic object model indicative of an object pose and an object shape for the dynamic object. The method can be repeated on sequential two- and three-dimensional information to generate a sequence of object model parameters over time. Portions of a sequence of parameters can be stored as simulation data descriptive of a simulated trajectory of a unique dynamic object. The parameters can be evaluated by an objective function to refine the parameters and train the machine-learned model.


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