  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 severe occlusions and extreme lighting conditions.
Inspired by human perception, the recognition of lanes under severe occlusions
and extreme lighting conditions is mainly based on contextual and global
information. Motivated by this observation, we propose a novel, simple, yet
effective formulation aiming at ultra fast speed and the problem of challenging
scenarios. Specifically, we treat the process of lane detection as an
anchor-driven ordinal classification problem using global features. First, we
represent lanes with sparse coordinates on a series of hybrid (row and column)
anchors. With the help of the anchor-driven representation, we then reformulate
the lane detection task as an ordinal classification problem to get the
coordinates of lanes. Our method could significantly reduce the computational
cost with the anchor-driven representation. Using the large receptive field
property of the ordinal classification formulation, we could also handle
challenging scenarios. Extensive experiments on four lane detection datasets
show that our method could achieve state-of-the-art performance in terms of
both speed and accuracy. A lightweight version could even achieve 300+ frames
per second(FPS). Our code is at
https://github.com/cfzd/Ultra-Fast-Lane-Detection-v2.
