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. 24, 2024

Filed:

Jun. 07, 2022
Applicant:

Mobiltech, Seoul, KR;

Inventors:

Jae Seung Kim, Seoul, KR;

Yeon Soo Park, Seoul, KR;

Assignee:

MOBILTECH, Seoul, KR;

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06V 20/56 (2022.01); G01S 7/48 (2006.01); G06T 7/11 (2017.01); G06T 11/00 (2006.01); G06V 10/774 (2022.01);
U.S. Cl.
CPC ...
G06V 20/588 (2022.01); G01S 7/4802 (2013.01); G06T 7/11 (2017.01); G06T 11/00 (2013.01); G06V 10/774 (2022.01); G06T 2207/10028 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20068 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30256 (2013.01); G06T 2210/32 (2013.01); G06T 2210/56 (2013.01);
Abstract

A lane extraction method uses projection transformation of a 3D point cloud map, by which the amount of operations required to extract the coordinates of a lane is reduced by performing deep learning and lane extraction in a two-dimensional (2D) domain, and therefore, lane information is obtained in real time. In addition, black-and-white brightness, which is most important information for lane extraction on an image, is substituted by the reflection intensity of a light detection and ranging (LiDAR) sensor so that a deep learning model capable of accurately extracting a lane is provided. Therefore, reliability and competitiveness is enhanced in the field of autonomous driving, the field of road recognition, the field of lane recognition, and the field of HD road maps for autonomous driving, and the fields similar or related thereto, and more particularly, in the fields of road recognition and autonomous driving using LiDAR.


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