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:
Oct. 22, 2024

Filed:

Jan. 26, 2022
Applicant:

Ford Global Technologies, Llc, Dearborn, MI (US);

Inventors:

Gaurab Banerjee, Chandler, AZ (US);

Vijay Nagasamy, Fremont, CA (US);

Assignee:

Ford Global Technologies, LLC, Dearborn, MI (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/00 (2022.01); G01S 13/86 (2006.01); G01S 13/89 (2006.01); G01S 15/86 (2020.01); G01S 15/89 (2006.01); G01S 17/86 (2020.01); G01S 17/89 (2020.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01); G01S 13/931 (2020.01); G01S 15/931 (2020.01); G01S 17/931 (2020.01);
U.S. Cl.
CPC ...
G06V 10/803 (2022.01); G01S 13/862 (2013.01); G01S 13/865 (2013.01); G01S 13/867 (2013.01); G01S 13/89 (2013.01); G01S 15/86 (2020.01); G01S 15/89 (2013.01); G01S 17/86 (2020.01); G01S 17/89 (2013.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01); G01S 13/931 (2013.01); G01S 15/931 (2013.01); G01S 17/931 (2020.01);
Abstract

A plurality of images can be acquired from a plurality of sensors and a plurality of flattened patches can be extracted from the plurality of images. An image location in the plurality of images and a sensor type token identifying a type of sensor used to acquire an image in the plurality of images from which the respective flattened patch was acquired can be added to each of the plurality of flattened patches. The flattened patches can be concatenated into a flat tensor and add a task token indicating a processing task to the flat tensor, wherein the flat tensor is a one-dimensional array that includes two or more types of data. The flat tensor can be input to a first deep neural network that includes a plurality of encoder layers and a plurality of decoder layers and outputs transformer output. The transformer output can be input to a second deep neural network that determines an object prediction indicated by the token and the object predictions can be output.


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