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:
Apr. 23, 2024

Filed:

Dec. 17, 2020
Applicant:

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

Inventors:

Greg Woelki, San Francisco, CA (US);

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

Bertrand Robert Douillard, Palo Alto, CA (US);

Michael Haggblade, El Dorado Hills, CA (US);

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

Assignee:

ZOOX, INC., Foster City, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G05D 1/02 (2020.01); B60W 60/00 (2020.01); G05D 1/00 (2006.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06V 20/58 (2022.01);
U.S. Cl.
CPC ...
G05D 1/0221 (2013.01); B60W 60/0027 (2020.02); B60W 60/005 (2020.02); G05D 1/0214 (2013.01); G05D 1/0231 (2013.01); G05D 1/0276 (2013.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06V 20/58 (2022.01); B60W 2420/42 (2013.01); B60W 2554/4026 (2020.02); B60W 2554/4029 (2020.02); B60W 2554/404 (2020.02); B60W 2556/10 (2020.02); B60W 2556/45 (2020.02); G05D 2201/0213 (2013.01);
Abstract

Techniques for determining a prediction probability associated with a disengagement event are discussed herein. A first prediction probability can include a probability that a safety driver associated with a vehicle (such as an autonomous vehicle) may assume control over the vehicle. A second prediction probability can include a probability that an object in an environment is associated the disengagement event. Sensor data can be captured and represented as a top-down representation of the environment. The top-down representation can be input to a machine learned model trained to output prediction probabilities associated with a disengagement event. The vehicle can be controlled based the prediction probability and/or the interacting object probability.


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