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:
Feb. 25, 2025

Filed:

Sep. 26, 2023
Applicant:

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

Inventors:

Robert Earl Rasmusson, San Francisco, CA (US);

Taggart Matthiesen, Kentfield, CA (US);

Craig Dehner, San Francisco, CA (US);

Linda Dong, San Francisco, CA (US);

Frank Taehyun Yoo, San Carlos, CA (US);

Karina van Schaardenburg, San Francisco, CA (US);

John Tighe, San Francisco, CA (US);

Matt Vitelli, San Francisco, CA (US);

Jisi Guo, San Francisco, CA (US);

Eli Guerron, San Francisco, CA (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
B60W 60/00 (2020.01); B62D 15/02 (2006.01); G01C 21/36 (2006.01); G05D 1/00 (2006.01); G06F 18/24 (2023.01); G06T 7/20 (2017.01); G06V 20/56 (2022.01);
U.S. Cl.
CPC ...
G01C 21/3638 (2013.01); B60W 60/00253 (2020.02); B62D 15/0285 (2013.01); G01C 21/365 (2013.01); G05D 1/0044 (2013.01); G05D 1/0088 (2013.01); G05D 1/0212 (2013.01); G05D 1/0246 (2013.01); G05D 1/0274 (2013.01); G06F 18/24 (2023.01); G06T 7/20 (2013.01); G06V 20/56 (2022.01); B60W 2420/403 (2013.01); B60W 2420/408 (2024.01); B60W 2554/00 (2020.02); B60W 2555/20 (2020.02); B60W 2556/10 (2020.02); G06T 2207/30252 (2013.01); G06T 2207/30256 (2013.01); G06T 2207/30261 (2013.01);
Abstract

In one embodiment, a method includes receiving sensor data associated with an object external to a vehicle. The sensor data includes a sequence of data packets. Each data packet corresponds to a time frame in a sequence of time frames. The method includes determining a classification of the object based at least on the sensor data, determining that a particular data packet corresponding to a particular time frame in the sequence of time frames is corrupt or missing, generating a replacement data packet based on one or more data packets that correspond to one or more time frames adjacent to the particular time frame, generating a sequence of visual representations of the object corresponding to the sequence of time frames. At least one visual representation in the sequence of visual representations corresponding to the particular time frame is generated based on the replacement data packet.


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