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:
May. 16, 2023

Filed:

Dec. 02, 2020
Applicant:

Nvidia Corporation, Santa Clara, CA (US);

Inventors:

Minwoo Park, Saratoga, CA (US);

Yilin Yang, Santa Clara, CA (US);

Xiaolin Lin, Sunnyvale, CA (US);

Abhishek Bajpayee, Santa Clara, CA (US);

Hae-Jong Seo, Campbell, CA (US);

Eric Jonathan Yuan, Menlo Park, CA (US);

Xudong Chen, Sunnyvale, CA (US);

Assignee:

NVIDIA Corporation, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2023.01); G06V 20/58 (2022.01); G06V 20/56 (2022.01); G06F 18/23 (2023.01); G06F 18/214 (2023.01); G06V 10/762 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 10/44 (2022.01); G06V 10/26 (2022.01); G06V 10/46 (2022.01); G05D 1/00 (2006.01); G06N 3/045 (2023.01); G06V 10/75 (2022.01); G06V 10/774 (2022.01); G06V 10/94 (2022.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G05D 1/0088 (2013.01); G06F 18/214 (2023.01); G06F 18/23 (2023.01); G06N 3/045 (2023.01); G06V 10/26 (2022.01); G06V 10/454 (2022.01); G06V 10/46 (2022.01); G06V 10/757 (2022.01); G06V 10/763 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 10/955 (2022.01); G06V 20/582 (2022.01); G06V 20/588 (2022.01); G05D 2201/0213 (2013.01); G06V 10/471 (2022.01);
Abstract

In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operation—e.g., Bezier curve fitting—to identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and/or three-dimensional (3D) world-space control point locations, and post-processing techniques—such as clustering and temporal smoothing—may be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarks—e.g., lane line, road boundary line, crosswalk, pole, text, etc.—may be used by a vehicle to perform one or more operations for navigating an environment.


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