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:
Jul. 30, 2024

Filed:

Jun. 05, 2023
Applicant:

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

Inventors:

Oytun Ulutan, Buena Park, CA (US);

Xin Wang, Sunnyvale, CA (US);

Kratarth Goel, Albany, CA (US);

Vasiliy Karasev, San Francisco, CA (US);

Sarah Tariq, Palo Alto, CA (US);

Yi Xu, Pasadena, CA (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 40/20 (2022.01); B60W 60/00 (2020.01); G05D 1/00 (2024.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06F 18/24 (2023.01); G06N 20/00 (2019.01); G06T 7/70 (2017.01); G06V 20/58 (2022.01); G06V 40/10 (2022.01);
U.S. Cl.
CPC ...
G06V 40/28 (2022.01); G06F 18/2148 (2023.01); G06F 18/217 (2023.01); G06F 18/24 (2023.01); G06T 7/70 (2017.01); G06V 20/582 (2022.01); G06V 40/103 (2022.01); G06V 40/23 (2022.01); B60W 60/001 (2020.02); B60W 2420/403 (2013.01); B60W 2540/041 (2020.02); G05D 1/0088 (2013.01); G05D 1/0231 (2013.01); G06N 20/00 (2019.01); G06T 2207/20081 (2013.01); G06T 2207/30196 (2013.01); G06T 2207/30252 (2013.01);
Abstract

Techniques for detecting attributes and/or gestures associated with pedestrians in an environment are described herein. The techniques may include receiving sensor data associated with a pedestrian in an environment of a vehicle and inputting the sensor data into a machine-learned model that is configured to determine a gesture and/or an attribute of the pedestrian. Based on the input data, an output may be received from the machine-learned model that indicates the gesture and/or the attribute of the pedestrian and the vehicle may be controlled based at least in part on the gesture and/or the attribute of the pedestrian. The techniques may also include training the machine-learned model to detect the attribute and/or the gesture of the pedestrian.


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