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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.

Patent No.:

US 12148227 B1

PDFFull Text
Date of Patent:
Nov. 19, 2024

Filed:

Jan. 27, 2023

System and method for large-scale lane marking detection using multimodal sensor data

Applicant:

Tusimple, Inc., San Diego, CA (US);

Inventors:

Xue Mei, San Diego, CA (US);

Xiaodi Hou, San Diego, CA (US);

Dazhou Guo, San Diego, CA (US);

Yujie Wei, San Diego, CA (US);

Assignee:

TUSIMPLE, INC., San Diego, CA (US);

Attorney:

Perkins Coie LLP

Primary Examiner:

Oneal R Mistry

Int. Cl.
CPC ...
G06T 7/10 (2017.01); B60R 11/04 (2006.01); G01C 21/00 (2006.01); G01S 17/42 (2006.01); G06N 3/02 (2006.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06T 5/50 (2006.01); G06T 7/246 (2017.01); G06V 20/56 (2022.01); G07C 5/00 (2006.01);
U.S. Cl.
CPC ...
G06V 20/588 (2022.01); B60R 11/04 (2013.01); G01C 21/3826 (2020.08); G01C 21/3833 (2020.08); G01C 21/3867 (2020.08); G01S 17/42 (2013.01); G06N 3/02 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06T 5/50 (2013.01); G06T 7/10 (2017.01); G06T 7/246 (2017.01); G07C 5/008 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/20221 (2013.01); G06T 2207/30256 (2013.01);
Abstract

A system and method for large-scale lane marking detection using multimodal sensor data are disclosed. A particular embodiment includes: receiving image data from an image generating device mounted on a vehicle; receiving point cloud data from a distance and intensity measuring device mounted on the vehicle; fusing the image data and the point cloud data to produce a set of lane marking points in three-dimensional (3D) space that correlate to the image data and the point cloud data; and generating a lane marking map from the set of lane marking points.

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