Lane detection is the task of detecting lanes on a road from a camera.
( Image credit: End-to-end Lane Detection )
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One of the main factors that contributed to the large advances in autonomous driving is the advent of deep learning.
The method, inspired by the latest state-of-the-art 3D-LaneNet, is a unified framework solving image encoding, spatial transform of features and 3D lane prediction in a single network.
In this paper, we propose a novel lane detection method for the arbitrary number of lanes using the deep learning method, which has the lower number of false positives than other recent lane detection methods.
SOTA for Lane Detection on TuSimple
Nowadays, deep learning techniques are widely used for lane detection, but application in low-light conditions remains a challenge until this day.
Training deep models for lane detection is challenging due to the very subtle and sparse supervisory signals inherent in lane annotations.
SOTA for Lane Detection on BDD100k
Lane detection is extremely important for autonomous vehicles.
#11 best model for Lane Detection on TuSimple
In this paper, we use lane detection to study modeling and training techniques that yield better performance on real world test drives.
Traditional algorithms usually estimate only the position of the lanes on the road, but an autonomous control system may also need to know if a lane marking can be crossed or not, and what portion of space inside the lane is free from obstacles, to make safer control decisions.