Structure-Aware Network for Lane Marker Extraction with Dynamic Vision Sensor
This work addresses lane marker extraction in extreme conditions like illumination changes for autonomous driving, but it is incremental as it adapts existing methods to a new sensor type.
The authors tackled lane marker extraction for autonomous driving by introducing a Dynamic Vision Sensor (DVS) dataset with 5,424 high-resolution images and a structure-aware network, which outperformed state-of-the-art models on this dataset.
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.