  Lane detection is one of the most important functions for autonomous driving.
In recent years, deep learning-based lane detection networks with RGB camera
images have shown promising performance. However, camera-based methods are
inherently vulnerable to adverse lighting conditions such as poor or dazzling
lighting. Unlike camera, LiDAR sensor is robust to the lighting conditions. In
this work, we propose a novel two-stage LiDAR lane detection network with
row-wise detection approach. The first-stage network produces lane proposals
through a global feature correlator backbone and a row-wise detection head.
Meanwhile, the second-stage network refines the feature map of the first-stage
network via attention-based mechanism between the local features around the
lane proposals, and outputs a set of new lane proposals. Experimental results
on the K-Lane dataset show that the proposed network advances the
state-of-the-art in terms of F1-score with 30% less GFLOPs. In addition, the
second-stage network is found to be especially robust to lane occlusions, thus,
demonstrating the robustness of the proposed network for driving in crowded
environments.
