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:
Jan. 30, 2024

Filed:

Mar. 31, 2020
Applicant:

Nvidia Corporation, Santa Clara, CA (US);

Inventors:

Alexander Popov, Kirkland, WA (US);

Nikolai Smolyanskiy, Seattle, WA (US);

Ryan Oldja, Redmond, WA (US);

Shane Murray, San Jose, CA (US);

Tilman Wekel, Sunnyvale, CA (US);

David Nister, Bellevue, WA (US);

Joachim Pehserl, Lynnwood, WA (US);

Ruchi Bhargava, Redmond, WA (US);

Sangmin Oh, San Jose, CA (US);

Assignee:

NVIDIA Corporation, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G01S 7/295 (2006.01); G06T 7/246 (2017.01); G06T 7/73 (2017.01); G01S 7/41 (2006.01); G01S 13/931 (2020.01); G06N 3/08 (2023.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01); G06V 20/64 (2022.01);
U.S. Cl.
CPC ...
G01S 7/2955 (2013.01); G01S 7/414 (2013.01); G01S 7/417 (2013.01); G01S 13/931 (2013.01); G06N 3/08 (2013.01); G06T 7/246 (2017.01); G06T 7/73 (2017.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01); G06V 20/64 (2022.01); G06T 2207/10044 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30261 (2013.01);
Abstract

In various examples, a deep neural network(s) (e.g., a convolutional neural network) may be trained to detect moving and stationary obstacles from RADAR data of a three dimensional (3D) space, in both highway and urban scenarios. RADAR detections may be accumulated, ego-motion-compensated, orthographically projected, and fed into a neural network(s). The neural network(s) may include a common trunk with a feature extractor and several heads that predict different outputs such as a class confidence head that predicts a confidence map and an instance regression head that predicts object instance data for detected objects. The outputs may be decoded, filtered, and/or clustered to form bounding shapes identifying the location, size, and/or orientation of detected object instances. The detected object instances may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.


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