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:
Jun. 18, 2024

Filed:

Jan. 31, 2020
Applicant:

Zoox, Inc., Foster City, CA (US);

Inventors:

Subhasis Das, Menlo Park, CA (US);

Benjamin Isaac Zwiebel, Burlingame, CA (US);

Kai Yu, Burlingame, CA (US);

James William Vaisey Philbin, Palo Alto, CA (US);

Assignee:

Zoox, Inc., Foster City, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
B60W 60/00 (2020.01); G01S 13/89 (2006.01); G01S 13/931 (2020.01); G01S 17/89 (2020.01); G01S 17/931 (2020.01); G05D 1/00 (2024.01); G06T 7/215 (2017.01); G06T 7/246 (2017.01); G06T 7/292 (2017.01); G06V 10/25 (2022.01); G06V 10/778 (2022.01); G06V 10/80 (2022.01); G06V 20/56 (2022.01); G06V 30/19 (2022.01); G06V 30/24 (2022.01);
U.S. Cl.
CPC ...
B60W 60/0027 (2020.02); G01S 13/89 (2013.01); G01S 13/931 (2013.01); G01S 17/89 (2013.01); G01S 17/931 (2020.01); G05D 1/0248 (2013.01); G06T 7/215 (2017.01); G06T 7/251 (2017.01); G06T 7/292 (2017.01); G06V 10/25 (2022.01); G06V 10/778 (2022.01); G06V 10/80 (2022.01); G06V 20/56 (2022.01); G06V 30/19147 (2022.01); G06V 30/19173 (2022.01); G06V 30/1918 (2022.01); G06V 30/2552 (2022.01); G01S 2013/932 (2020.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30241 (2013.01); G06T 2207/30261 (2013.01);
Abstract

Tracking a current and/or previous position, velocity, acceleration, and/or heading of an object using sensor data may comprise determining whether to associate a current object detection generated from recently received (e.g., current) sensor data with a previous object detection generated from formerly received sensor data. In other words, a track may identify that an object detected in former sensor data is the same object detected in current sensor data. However, multiple types of sensor data may be used to detect objects and some objects may not be detected by different sensor types or may be detected differently, which may confound attempts to track an object. An ML model may be trained to receive outputs associated with different sensor types and/or a track associated with an object, and determine a data structure comprising a region of interest, object classification, and/or a pose associated with the object.


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