Junsuk M oon Grid Defined Lane Detection

Abstract

Lane lines contain a lot of information beyond informing the direction of the vehicle's progress when driving on the road. The direction of driving of other vehicles and furthermore, traffic rules have meaning. So it is very important to detect it. In autonomous vehicles, lane detection is a task of providing essential information for vehicle control. Lane detection is used for vehicles to locate themselves on the road and maintain the correct path. However, lanes occupy a very small part of the overall image and have a wide variety of forms depending on the road environment. Detection is often difficult depending on weather conditions and the surrounding environment. In this paper, we propose a method to detect the overall lane by overcoming these challenges and restoring even the distorted part. This pipeline, which creates multiple lane candidates and allows each lane candidate to find the actual shape of the lane, consists of four modules: lane point regressor, lane point classifier, lane set suppressor, and lane set classifier. Through this pipeline, we introduce a novel methodology in which lane candidates correspond to all lanes in the image by selecting a label lane to correspond to by L1 distance calculation and having a shape that matches the label lane.