  Lane detection is a critical function for autonomous driving. With the recent
development of deep learning and the publication of camera lane datasets and
benchmarks, camera lane detection networks (CLDNs) have been remarkably
developed. Unfortunately, CLDNs rely on camera images which are often distorted
near the vanishing line and prone to poor lighting condition. This is in
contrast with Lidar lane detection networks (LLDNs), which can directly extract
the lane lines on the bird's eye view (BEV) for motion planning and operate
robustly under various lighting conditions. However, LLDNs have not been
actively studied, mostly due to the absence of large public lidar lane
datasets. In this paper, we introduce KAIST-Lane (K-Lane), the world's first
and the largest public urban road and highway lane dataset for Lidar. K-Lane
has more than 15K frames and contains annotations of up to six lanes under
various road and traffic conditions, e.g., occluded roads of multiple occlusion
levels, roads at day and night times, merging (converging and diverging) and
curved lanes. We also provide baseline networks we term Lidar lane detection
networks utilizing global feature correlator (LLDN-GFC). LLDN-GFC exploits the
spatial characteristics of lane lines on the point cloud, which are sparse,
thin, and stretched along the entire ground plane of the point cloud. From
experimental results, LLDN-GFC achieves the state-of-the-art performance with
an F1- score of 82.1%, on the K-Lane. Moreover, LLDN-GFC shows strong
performance under various lighting conditions, which is unlike CLDNs, and also
robust even in the case of severe occlusions, unlike LLDNs using the
conventional CNN. The K-Lane, LLDN-GFC training code, pre-trained models, and
complete development kits including evaluation, visualization and annotation
tools are available at https://github.com/kaist-avelab/k-lane.
