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 …

State-of-the-art lane detection methods often rely on specific knowledge about lanes--such
as straight lines and parametric curves--to detect lane lines. While the specific knowledge …

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 …

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 …

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 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 playing an indispensable role in advanced driver assistance systems. The
existing approaches for lane detection can be categorized as lane area segmentation and …

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 …