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 …

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 …

Abstract 3D lane detection which plays a crucial role in vehicle routing, has recently been a
rapidly developing topic in autonomous driving. Previous works struggle with practicality due …

Monocular 3D lane detection is a challenging task due to its lack of depth information. A
popular solution is to first transform the front-viewed (FV) images or features into the bird-eye …

Abstract 3D lane detection from monocular images is a fundamental yet challenging task in
autonomous driving. Recent advances primarily rely on structural 3D surrogates (eg, bird's …

We present ONCE-3DLanes, a real-world autonomous driving dataset with lane layout
annotation in 3D space. Conventional 2D lane detection from a monocular image yields …

This study presents an approach to lane detection involving the prediction of binary
segmentation masks and per-pixel affinity fields. These affinity fields, along with the binary …

High-definition (HD) vectorized map is essential for autonomous driving, providing detailed
and precise environmental information for advanced perception and planning. However …

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

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 …