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:
Dec. 31, 2024

Filed:

Dec. 06, 2021
Applicant:

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

Inventors:

Xi Joey Hong, Campbell, CA (US);

Benjamin John Sapp, San Francisco, CA (US);

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

Kai Zhenyu Wang, Foster City, CA (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G08G 1/16 (2006.01); B60W 30/095 (2012.01); G05D 1/00 (2006.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01); G06T 7/292 (2017.01);
U.S. Cl.
CPC ...
G08G 1/164 (2013.01); B60W 30/0956 (2013.01); G05D 1/0221 (2013.01); G05D 1/0276 (2013.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06T 7/292 (2017.01); G08G 1/166 (2013.01); G06T 2207/10032 (2013.01); G06T 2207/30236 (2013.01); G06T 2207/30241 (2013.01);
Abstract

Techniques are discussed for determining prediction probabilities of an object based on a top-down representation of an environment. Data representing objects in an environment can be captured. Aspects of the environment can be represented as map data. A multi-channel image representing a top-down view of object(s) in the environment can be generated based on the data representing the objects and map data. The multi-channel image can be used to train a machine learned model by minimizing an error between predictions from the machine learned model and a captured trajectory associated with the object. Once trained, the machine learned model can be used to generate prediction probabilities of objects in an environment, and the vehicle can be controlled based on such prediction probabilities.


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