  Lane detection is challenging due to the complicated on road scenarios and
line deformation from different camera perspectives. Lots of solutions were
proposed, but can not deal with corner lanes well. To address this problem,
this paper proposes a new top-down deep learning lane detection approach,
CANET. A lane instance is first responded by the heat-map on the U-shaped
curved guide line at global semantic level, thus the corresponding features of
each lane are aggregated at the response point. Then CANET obtains the heat-map
response of the entire lane through conditional convolution, and finally
decodes the point set to describe lanes via adaptive decoder. The experimental
results show that CANET reaches SOTA in different metrics. Our code will be
released soon.
