  Lane detection is one of the most important tasks in self-driving. Due to
various complex scenarios (e.g., severe occlusion, ambiguous lanes, etc.) and
the sparse supervisory signals inherent in lane annotations, lane detection
task is still challenging. Thus, it is difficult for the ordinary convolutional
neural network (CNN) to train in general scenes to catch subtle lane feature
from the raw image. In this paper, we present a novel module named REcurrent
Feature-Shift Aggregator (RESA) to enrich lane feature after preliminary
feature extraction with an ordinary CNN. RESA takes advantage of strong shape
priors of lanes and captures spatial relationships of pixels across rows and
columns. It shifts sliced feature map recurrently in vertical and horizontal
directions and enables each pixel to gather global information. RESA can
conjecture lanes accurately in challenging scenarios with weak appearance clues
by aggregating sliced feature map. Moreover, we propose a Bilateral Up-Sampling
Decoder that combines coarse-grained and fine-detailed features in the
up-sampling stage. It can recover the low-resolution feature map into
pixel-wise prediction meticulously. Our method achieves state-of-the-art
results on two popular lane detection benchmarks (CULane and Tusimple). Code
has been made available at: https://github.com/ZJULearning/resa.
