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. 13, 2025

Filed:

Dec. 20, 2023
Applicant:

Nvidia Corporation, Santa Clara, CA (US);

Inventors:

Trung Pham, Santa Clara, CA (US);

Berta Rodriguez Hervas, San Francisco, CA (US);

Minwoo Park, Saratoga, CA (US);

David Nister, Bellevue, WA (US);

Neda Cvijetic, East Palo Alto, CA (US);

Assignee:

NVIDIA Corporation, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/11 (2017.01); G05B 13/02 (2006.01); G06F 18/21 (2023.01); G06F 18/24 (2023.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06T 3/4046 (2024.01); G06T 5/70 (2024.01); G06T 11/20 (2006.01); G06V 10/26 (2022.01); G06V 10/34 (2022.01); G06V 10/44 (2022.01); G06V 10/82 (2022.01); G06V 20/56 (2022.01); G06V 30/19 (2022.01); G06V 30/262 (2022.01);
U.S. Cl.
CPC ...
G06T 7/11 (2017.01); G05B 13/027 (2013.01); G06F 18/21 (2023.01); G06F 18/24 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06T 3/4046 (2013.01); G06T 5/70 (2024.01); G06T 11/20 (2013.01); G06V 10/267 (2022.01); G06V 10/34 (2022.01); G06V 10/454 (2022.01); G06V 10/82 (2022.01); G06V 20/56 (2022.01); G06V 30/19173 (2022.01); G06V 30/274 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30252 (2013.01); G06T 2210/12 (2013.01);
Abstract

In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersection contention areas in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute outputs—such as signed distance functions—that may correspond to locations of boundaries delineating intersection contention areas. The signed distance functions may be decoded and/or post-processed to determine instance segmentation masks representing locations and classifications of intersection areas or regions. The locations of the intersections areas or regions may be generated in image-space and converted to world-space coordinates to aid an autonomous or semi-autonomous vehicle in navigating intersections according to rules of the road, traffic priority considerations, and/or the like.


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