Lane is critical in the vision navigation system of the intelligent vehicle. Naturally, lane is a
traffic sign with high-level semantics, whereas it owns the specific local pattern which needs …

Modern methods mainly regard lane detection as a problem of pixel-wise segmentation,
which is struggling to address the problem of challenging scenarios and speed. Inspired by …

Lane detection is a challenging task that requires predicting complex topology shapes of
lane lines and distinguishing different types of lanes simultaneously. Earlier works follow a …

Modern deep-learning-based lane detection methods are successful in most scenarios but
struggling for lane lines with complex topologies. In this work, we propose CondLaneNet, a …

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 …

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 …

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 …

One of the main factors that contributed to the large advances in autonomous driving is the
advent of deep learning. For safer self-driving vehicles, one of the problems that has yet to …

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