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. 12, 2023

Filed:

Oct. 23, 2020
Applicant:

Nvidia Corporation, Santa Clara, CA (US);

Inventors:

Mark Damon Wheeler, Saratoga, CA (US);

Xiaqing Wu, Foster City, CA (US);

Assignee:

NVIDIA CORPORATION, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
B60W 30/00 (2006.01); G06V 10/764 (2022.01); G06F 16/29 (2019.01); H04L 67/10 (2022.01); H04L 69/40 (2022.01); G01C 21/00 (2006.01); G06V 10/98 (2022.01); G06V 20/56 (2022.01); G06V 20/64 (2022.01); H04L 67/52 (2022.01); G06F 18/22 (2023.01); G06F 18/24 (2023.01); G06V 10/75 (2022.01); G01C 21/36 (2006.01); G05D 1/00 (2006.01); G06T 17/05 (2011.01); B60W 30/095 (2012.01); B60W 40/02 (2006.01); G01C 21/30 (2006.01); G05D 1/02 (2020.01); H04L 67/12 (2022.01); G06V 20/58 (2022.01); H04L 67/53 (2022.01);
U.S. Cl.
CPC ...
G06V 10/764 (2022.01); B60W 30/0956 (2013.01); B60W 40/02 (2013.01); G01C 21/30 (2013.01); G01C 21/3635 (2013.01); G01C 21/3841 (2020.08); G01C 21/3848 (2020.08); G01C 21/3867 (2020.08); G05D 1/0088 (2013.01); G05D 1/0214 (2013.01); G05D 1/0274 (2013.01); G06F 16/29 (2019.01); G06F 18/22 (2023.01); G06F 18/24 (2023.01); G06T 17/05 (2013.01); G06V 10/75 (2022.01); G06V 10/98 (2022.01); G06V 20/56 (2022.01); G06V 20/64 (2022.01); H04L 67/10 (2013.01); H04L 67/12 (2013.01); H04L 67/52 (2022.05); H04L 69/40 (2013.01); B60W 2554/00 (2020.02); B60W 2556/50 (2020.02); G05D 2201/0213 (2013.01); G06V 20/58 (2022.01); H04L 67/53 (2022.05);
Abstract

An online system builds a high definition (HD) map for a geographical region based on sensor data captured by a plurality of autonomous vehicles driving through a geographical region. The autonomous vehicles detect map discrepancies based on differences in the surroundings observed using sensor data compared to the high definition map and send messages describing these map discrepancies to the online system. The online system updates existing occupancy maps to improve the accuracy of the occupancy maps (OMaps), and to thereby improve passenger and pedestrian safety. While vehicles are in motion, they can continuously collect data about their surroundings. When new data is available from the various vehicles within a fleet, this can be updated in a local representation of the occupancy map and can be passed to the online HD map system (e.g., in the cloud) for updating the master occupancy map shared by all of the vehicles.


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