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. 15, 2023

Filed:

Nov. 07, 2022
Applicant:

Argo Ai, Llc, Pittsburgh, PA (US);

Inventors:

Rotem Littman, Hod Hasharon, IL;

Gilad Saban, Rehovot, IL;

Noam Presman, Ramat Gan, IL;

Dana Berman, Tel Aviv, IL;

Asaf Kagan, Herzliya, IL;

Assignee:

ARGO AI, LLC, Pittsburgh, PA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 11/20 (2006.01); G06V 10/44 (2022.01); G06T 7/70 (2017.01); G06T 7/20 (2017.01); G06T 7/00 (2017.01); G06T 7/90 (2017.01); G05D 1/02 (2020.01); G05D 1/00 (2006.01); G06V 20/58 (2022.01); G06V 20/40 (2022.01); G06F 18/24 (2023.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 10/75 (2022.01);
U.S. Cl.
CPC ...
G06V 10/454 (2022.01); G05D 1/0088 (2013.01); G05D 1/0231 (2013.01); G06F 18/24 (2023.01); G06T 7/20 (2013.01); G06T 7/70 (2017.01); G06T 7/90 (2017.01); G06T 7/97 (2017.01); G06T 11/20 (2013.01); G06V 10/758 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/41 (2022.01); G06V 20/58 (2022.01); G06V 20/584 (2022.01); G05D 2201/0213 (2013.01); G06T 2207/10016 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30244 (2013.01); G06T 2207/30252 (2013.01); G06T 2210/12 (2013.01);
Abstract

Disclosed herein are systems, methods, and computer program products for predicting movement of an object in a real-world environment. The methods comprise: obtaining a plurality of image frames captured in a sequence during a period of time; identifying first image frames of the plurality of image frames that contain an image of at least one object with one or more turn signals; analyzing the first image frames to obtain a classification for a pose of the at least one object; using the classification of the pose of the at least one object to further obtain a type classification for at least one of the turn signals and a state classification for a state of at least one of the turn signals; and predicting movement of the at least one object based at least on the type and state classifications obtained for at least one of the turn signals.


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