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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.

Patent No.:

US 11922708 B1

PDFFull Text
Date of Patent:
Mar. 05, 2024

Filed:

Sep. 12, 2022

Multiple stage image based object detection and recognition

Applicant:

Uatc, Llc, Mountain View, CA (US);

Inventors:

Carlos Vallespi-Gonzalez, Pittsburgh, PA (US);

Joseph Lawrence Amato, Pittsburgh, PA (US);

George Totolos, Jr., Cranberry Township, PA (US);

Assignee:

UATC, LLC, Mountain View, CA (US);

Attorney:

Dority & Manning, P.A.

Primary Examiner:

Xin Jia

Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06F 18/214 (2023.01); G06F 18/241 (2023.01); G06F 18/243 (2023.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06T 7/521 (2017.01); G06T 15/08 (2011.01); G06V 10/28 (2022.01); G06V 10/50 (2022.01); G06V 10/56 (2022.01); G06V 10/764 (2022.01); G06V 20/58 (2022.01); G06V 20/64 (2022.01); G05D 1/00 (2006.01);
U.S. Cl.
CPC ...
G06V 20/64 (2022.01); G06F 18/214 (2023.01); G06F 18/241 (2023.01); G06F 18/24323 (2023.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06T 7/521 (2017.01); G06T 15/08 (2013.01); G06V 10/28 (2022.01); G06V 10/50 (2022.01); G06V 10/56 (2022.01); G06V 10/764 (2022.01); G06V 20/58 (2022.01); G06V 20/584 (2022.01); G05D 1/0238 (2013.01); G05D 2201/0213 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30261 (2013.01); G06T 2210/12 (2013.01);
Abstract

Systems, methods, tangible non-transitory computer-readable media, and devices for autonomous vehicle operation are provided. For example, a computing system can receive object data that includes portions of sensor data. The computing system can determine, in a first stage of a multiple stage classification using hardware components, one or more first stage characteristics of the portions of sensor data based on a first machine-learned model. In a second stage of the multiple stage classification, the computing system can determine second stage characteristics of the portions of sensor data based on a second machine-learned model. The computing system can generate an object output based on the first stage characteristics and the second stage characteristics. The object output can include indications associated with detection of objects in the portions of sensor data.

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