The search for predictive models that generalize to the long tail of sensor inputs is the
central difficulty when developing data-driven models for autonomous vehicles. In this …

The image-based lane detection algorithm is one of the key technologies in autonomous
vehicles. Modern deep learning methods achieve high performance in lane detection, but it …

Lane detection, the process of identifying lane markings as approximated curves, is widely
used for lane departure warning and adaptive cruise control in autonomous vehicles. The …

Lane detection is to detect lanes on the road and provide the accurate location and shape of
each lane. It severs as one of the key techniques to enable modern assisted and …

This paper presents a novel parametric curve-based method for lane detection in RGB
images. Unlike state-of-the-art segmentation-based and point detection-based methods that …

Lane detection is typically tackled with a two-step pipeline in which a segmentation mask of
the lane markings is predicted first, and a lane line model like a parabola or spline is fitted to …

Modern lane detection methods have achieved remarkable performances in complex real-
world scenarios, but many have issues maintaining real-time efficiency, which is important …

Lane detection is one of the most important tasks in self-driving. Due to various complex
scenarios (eg, severe occlusion, ambiguous lanes, etc.) and the sparse supervisory signals …

Recently, lane detection has made great progress with the rapid development of deep
neural networks and autonomous driving. However, there exist three mainly problems …

Modern methods mainly regard lane detection as a problem of pixel-wise segmentation,
which is struggling to address the problems of efficiency and challenging scenarios like …