  Lane marker extraction is a basic yet necessary task for autonomous driving.
Although past years have witnessed major advances in lane marker extraction
with deep learning models, they all aim at ordinary RGB images generated by
frame-based cameras, which limits their performance in extreme cases, like huge
illumination change. To tackle this problem, we introduce Dynamic Vision Sensor
(DVS), a type of event-based sensor to lane marker extraction task and build a
high-resolution DVS dataset for lane marker extraction. We collect the raw
event data and generate 5,424 DVS images with a resolution of 1280$\times$800
pixels, the highest one among all DVS datasets available now. All images are
annotated with multi-class semantic segmentation format. We then propose a
structure-aware network for lane marker extraction in DVS images. It can
capture directional information comprehensively with multidirectional slice
convolution. We evaluate our proposed network with other state-of-the-art lane
marker extraction models on this dataset. Experimental results demonstrate that
our method outperforms other competitors. The dataset is made publicly
available, including the raw event data, accumulated images and labels.
