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:
Feb. 06, 2024

Filed:

Feb. 27, 2023
Applicant:

Plusai, Inc., Santa Clara, CA (US);

Inventor:

Mianwei Zhou, Santa Clara, CA (US);

Assignee:

PlusAI, Inc., Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/774 (2022.01); G06T 11/00 (2006.01); G06V 10/82 (2022.01); G06V 10/776 (2022.01); G06T 11/20 (2006.01); B60W 60/00 (2020.01); G06V 20/56 (2022.01);
U.S. Cl.
CPC ...
G06V 10/774 (2022.01); B60W 60/001 (2020.02); G06T 11/001 (2013.01); G06T 11/203 (2013.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01); G06V 20/588 (2022.01); B60W 2300/145 (2013.01); B60W 2420/42 (2013.01); B60W 2530/10 (2013.01); B60W 2552/53 (2020.02);
Abstract

A user-generated graphical representation can be sent into a generative network to generate a synthetic image of an area including a road, the user-generated graphical representation including at least three different colors and each color from the at least three different colors representing a feature from a plurality of features. A determination can be made that a discrimination network fails to distinguish between the synthetic image and a sensor detected image. The synthetic image can be sent, in response to determining that the discrimination network fails to distinguish between the synthetic image and the sensor-detected image, into an object detector to generate a non-user-generated graphical representation. An objective function can be determined based on a comparison between the user-generated graphical representation and the non-user-generated graphical representation. A perception model can be trained using the synthetic image in response to determining that the objective function is within a predetermined acceptable range.


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