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:
Aug. 11, 2026

Filed:

Dec. 14, 2022
Applicant:

Kodiak Robotics, Inc., Mountain View, CA (US);

Inventors:

Suchir Gupta, Ada, MI (US);

Collin C. Otis, Driggs, ID (US);

Cole M. Miles, Mountain View, CA (US);

Philip C. Du Toit, Fort Collins, CO (US);

Andreas Wendel, Mountain View, CA (US);

Assignee:

Kodiak Robotics, Inc., Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
B60Q 1/28 (2006.01); B60Q 1/30 (2006.01); B60Q 1/32 (2006.01); B60W 30/17 (2020.01); B60W 40/00 (2006.01); G06N 20/00 (2019.01); G06V 20/58 (2022.01);
U.S. Cl.
CPC ...
G06V 20/58 (2022.01); G06N 20/00 (2019.01);
Abstract

This disclosure provides methods and systems for detecting and tracking objects in an environment of an autonomous vehicle. The method may include: receiving sensor data from at least one sensor of the autonomous vehicle, the sensor data representative of one or more portions of an object in the environment of the autonomous vehicle; determining a highest confidence portion of the object, wherein the highest confidence portion of the object comprises a portion of the object that is observed and estimated with highest accuracy and confidence; determining features of the highest confidence portion of the object; training a machine learning model based at least in part on the features of the highest confidence portion of the object and an error metric that measures difference between the highest confidence portion of the object and a corresponding portion of the object in a ground truth; and detecting or tracking one or more objects in the environment of the autonomous vehicle using the trained machine learning model.


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