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. 27, 2024

Filed:

Dec. 23, 2019
Applicant:

Lyft, Inc., San Francisco, CA (US);

Inventors:

Garrett Korda Drayna, San Carlos, CA (US);

Jens Paul Windau, San Francisco, CA (US);

Assignee:

Lyft, Inc., San Francisco, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G05D 1/02 (2020.01); B60L 50/20 (2019.01); G05B 13/02 (2006.01); G05D 1/00 (2006.01); G06N 3/04 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01); G06V 20/56 (2022.01); G07C 5/08 (2006.01); B60L 15/20 (2006.01); G06Q 30/04 (2012.01);
U.S. Cl.
CPC ...
B60L 50/20 (2019.02); G05B 13/027 (2013.01); G05D 1/0231 (2013.01); G05D 1/027 (2013.01); G05D 1/0291 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06V 10/803 (2022.01); G06V 10/82 (2022.01); G06V 20/56 (2022.01); G07C 5/0808 (2013.01); G07C 5/0825 (2013.01); G07C 5/0833 (2013.01); G07C 5/085 (2013.01); B60L 15/20 (2013.01); G05D 1/0278 (2013.01); G06Q 30/04 (2013.01);
Abstract

Techniques are disclosed for systems and methods for surface detection and fleet vehicle control. A method for controlling operations for a plurality of fleet vehicles, the method includes receiving, by a fleet vehicle of the plurality of fleet vehicles using camera-sensor fusion module, first image data of an operational surface in a field of view of the camera-sensor fusion module and inertial data of the fleet vehicle, determining, by the fleet vehicle using a fusion algorithm based on a neural network model, a surface classification of the operational surface based on the first image data and the inertial data, determining an operational parameter for the fleet vehicle based, at least in part, on the surface classification, and controlling an operation of the fleet vehicle based, at least in part, on the operational parameter.


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