  Lane detection plays a key role in autonomous driving. While car cameras
always take streaming videos on the way, current lane detection works mainly
focus on individual images (frames) by ignoring dynamics along the video. In
this work, we collect a new video instance lane detection (VIL-100) dataset,
which contains 100 videos with in total 10,000 frames, acquired from different
real traffic scenarios. All the frames in each video are manually annotated to
a high-quality instance-level lane annotation, and a set of frame-level and
video-level metrics are included for quantitative performance evaluation.
Moreover, we propose a new baseline model, named multi-level memory aggregation
network (MMA-Net), for video instance lane detection. In our approach, the
representation of current frame is enhanced by attentively aggregating both
local and global memory features from other frames. Experiments on the new
collected dataset show that the proposed MMA-Net outperforms state-of-the-art
lane detection methods and video object segmentation methods. We release our
dataset and code at https://github.com/yujun0-0/MMA-Net.
