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. 20, 2024

Filed:

Nov. 19, 2021
Applicant:

Hl Klemove Corp., Pyeongtaek, KR;

Inventors:

Vinuchackravarthy S., Bangalore, IN;

Shubham Jain, Bangalore, IN;

Assignee:

HL KLEMOVE CORP., Incheon, KR;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
B60W 50/14 (2020.01); B60W 30/12 (2020.01); B60W 30/18 (2012.01); G06N 3/08 (2023.01); G06V 10/26 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/56 (2022.01); G06V 20/58 (2022.01);
U.S. Cl.
CPC ...
G06V 20/588 (2022.01); B60W 30/12 (2013.01); B60W 30/18163 (2013.01); B60W 50/14 (2013.01); G06N 3/08 (2013.01); G06V 10/26 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); B60W 2420/403 (2013.01);
Abstract

The present disclosure is related in general to field of machine learning and image processing, that provides a method and system for detecting and classifying lanes. A lane detection and classification system receives an input image from a data source and segments the input image into plurality of segments using a trained semantic segmentation model. Further, one or more lane markings are detected in the segmented image and, lane pattern and lane colour of each of the one or more lane markings, wherein each lane marking is associated with a priority. Subsequently, a binary image comprising lane markings of ego lanes of the host vehicle is generated and coefficient values of the ego lanes of the host vehicle are determined based on the priority value associated with the lane markings of the ego lanes and current position of the host vehicle, using a trained Convolutional Neural Network (CNN) model.


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