US 12,067,788 B2
Method and system for detecting and classifying lanes
Vinuchackravarthy S., Bangalore (IN); and Shubham Jain, Bangalore (IN)
Assigned to HL KLEMOVE CORP., Incheon (KR)
Filed by HL Klemove Corp., Pyeongtaek (KR)
Filed on Nov. 19, 2021, as Appl. No. 17/530,524.
Claims priority of application No. 202041050636 (IN), filed on Nov. 20, 2020.
Prior Publication US 2022/0165072 A1, May 26, 2022
Int. Cl. 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)
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)] 18 Claims
OG exemplary drawing
 
1. A lane detection and classification system for detecting and classifying lane patterns, the lane detection and 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 an input image from a data source, wherein the input image is an RGB image captured by the image sensor;
segment the input image into plurality of segments using a trained semantic segmentation model;
detect one or more lane markings in the segmented image and, lane pattern and lane colour of each of the one or more lane markings, wherein each of the one or more lane markings is associated with a priority value based on the corresponding lane pattern and lane colour;
generate a binary image comprising lane markings of ego lanes of the host vehicle, wherein the lane markings of ego lanes are extracted from the one or more lane markings detected in the segmented image; and
determine coefficient values of the ego lanes of the host vehicle 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.