  Lane detection is one of the core functions in autonomous driving and has
aroused widespread attention recently. The networks to segment lane instances,
especially with bad appearance, must be able to explore lane distribution
properties. Most existing methods tend to resort to CNN-based techniques. A few
have a try on incorporating the recent adorable, the seq2seq Transformer
\cite{transformer}. However, their innate drawbacks of weak global information
collection ability and exorbitant computation overhead prohibit a wide range of
the further applications. In this work, we propose Atrous Transformer
(AtrousFormer) to solve the problem. Its variant local AtrousFormer is
interleaved into feature extractor to enhance extraction. Their collecting
information first by rows and then by columns in a dedicated manner finally
equips our network with stronger information gleaning ability and better
computation efficiency. To further improve the performance, we also propose a
local semantic guided decoder to delineate the identities and shapes of lanes
more accurately, in which the predicted Gaussian map of the starting point of
each lane serves to guide the process. Extensive results on three challenging
benchmarks (CULane, TuSimple, and BDD100K) show that our network performs
favorably against the state of the arts.
