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 …

In this paper, we revisit the limitations of anchor-based lane detection methods, which have
predominantly focused on fixed anchors that stem from the edges of the image, disregarding …

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 …

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 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 …

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 …

Detecting lane lines from sensors is becoming an increasingly significant part of
autonomous driving systems. However, less development has been made on high-definition …

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 …

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 …

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 …