US 12,136,274 B2
Method and system for detecting lane pattern
Varsha Singal, Bangalore (IN); Arpit Awasthi, Gurgaon (IN); and Jitesh Kumar Singh, Bangalore (IN)
Assigned to HL KLEMOVE CORP., Incheon (KR)
Filed by HL Klemove Corp., Pyeongtaek (KR)
Filed on Nov. 19, 2021, as Appl. No. 17/530,526.
Claims priority of application No. 202041050782 (IN), filed on Nov. 20, 2020.
Prior Publication US 2022/0164584 A1, May 26, 2022
Int. Cl. G06V 20/56 (2022.01); B60W 30/12 (2020.01); G06V 10/50 (2022.01)
CPC G06V 20/588 (2022.01) [G06V 10/50 (2022.01); B60W 30/12 (2013.01); B60W 2420/403 (2013.01); B60W 2552/53 (2020.02)] 11 Claims
OG exemplary drawing
 
1. A lane classification system for detecting lane pattern, the lane classification system comprising:
an image sensor mounted to a host vehicle, to capture images of an area in front of the host vehicle;
a processor communicatively connected to the image sensor and configured to:
receive a plurality of training images from the image sensor, wherein each of the plurality of training images comprises one or more lane markings and co-ordinates of the one or more lane markings;
divide each of the plurality of training image into two portions, a first image portion and a second image portion, wherein the first image portion is determined based on the one or more lane markings and the co-ordinates of the one or more lane markings located on a left side of each of the plurality of training images and the second image portion is determined based on the one or more lane markings and the co-ordinates of the one or more lane markings located on a right side of each of the plurality of training images;
resize the first image portion and the second image portion into a predefined template size;
train a first machine learning model using the resized first image portion of each of plurality of training images and a second machine learning model using the resized second image portion of each of plurality of training images;
receive an input image from the image sensor and divide the input image into the first image portion of the input image and the second image portion of the input image;
detect a lane pattern of the input image by inputting the first image portion of the input image to the first machine learning model and the second image portion of the input image to the second machine learning model; and
control a navigation of the host vehicle based on the detected lane pattern.