  A number of lane detection methods depend on a proposal-free instance
segmentation because of its adaptability to flexible object shape, occlusion,
and real-time application. This paper addresses the problem that pixel
embedding in proposal-free instance segmentation based lane detection is
difficult to optimize. A translation invariance of convolution, which is one of
the supposed strengths, causes challenges in optimizing pixel embedding. In
this work, we propose a lane detection method based on proposal-free instance
segmentation, directly optimizing spatial embedding of pixels using image
coordinate. Our proposed method allows the post-processing step for center
localization and optimizes clustering in an end-to-end manner. The proposed
method enables real-time lane detection through the simplicity of
post-processing and the adoption of a lightweight backbone. Our proposed method
demonstrates competitive performance on public lane detection datasets.
