  This paper presents a novel parametric curve-based method for lane detection
in RGB images. Unlike state-of-the-art segmentation-based and point
detection-based methods that typically require heuristics to either decode
predictions or formulate a large sum of anchors, the curve-based methods can
learn holistic lane representations naturally. To handle the optimization
difficulties of existing polynomial curve methods, we propose to exploit the
parametric B\'ezier curve due to its ease of computation, stability, and high
freedom degrees of transformations. In addition, we propose the deformable
convolution-based feature flip fusion, for exploiting the symmetry properties
of lanes in driving scenes. The proposed method achieves a new state-of-the-art
performance on the popular LLAMAS benchmark. It also achieves favorable
accuracy on the TuSimple and CULane datasets, while retaining both low latency
(> 150 FPS) and small model size (< 10M). Our method can serve as a new
baseline, to shed the light on the parametric curves modeling for lane
detection. Codes of our model and PytorchAutoDrive: a unified framework for
self-driving perception, are available at:
https://github.com/voldemortX/pytorch-auto-drive .
