Accurate 3D lane estimation is crucial for ensuring safety in autonomous driving. However,
prevailing monocular techniques suffer from depth loss and lighting variations, hampering …

Lane detection plays a vital part in autonomous driving. Conventional studies rely on less
robust hand-craft features, while deep learning has improved the performance of lane …

Lane detection is a fundamental task for autonomous driving. Most existed deep learning-
based methods use a combination of semantic segmentation and post-processing for lane …

Lane detection, which relies on front-view RGB cameras, is a crucial aspect of Advanced
Driver Assistance Systems (ADAS), but its effectiveness is notably reduced in low-light …

Lane detection is an important and fundamental task in autonomous driving. Modern
convolutional neural network (CNN) methods have achieved high performance in lane …

Accurately detecting the lanes plays a significant role in various autonomous and assistant
driving scenarios. It is a highly structured task as lanes in the 3D world are continuous and …

Lane detection is to determine the precise location and shape of lanes on the road. Despite
efforts made by current methods, it remains a challenging task due to the complexity of real …

Lane detection plays a pivotal role in the successful implementation of Advanced Driver
Assistance Systems (ADASs), which are essential for detecting the road's lane markings and …

Understanding the driving scene is critical for the safe operation of autonomous vehicles
with state-of-the-art (SoTA) systems relying upon a combination of different algorithms to …

Lane detection requires adequate global information due to the simplicity of lane line
features and changeable road scenes. In this paper, we propose a novel lane detection …