  Lane detection, the process of identifying lane markings as approximated
curves, is widely used for lane departure warning and adaptive cruise control
in autonomous vehicles. The popular pipeline that solves it in two steps --
feature extraction plus post-processing, while useful, is too inefficient and
flawed in learning the global context and lanes' long and thin structures. To
tackle these issues, we propose an end-to-end method that directly outputs
parameters of a lane shape model, using a network built with a transformer to
learn richer structures and context. The lane shape model is formulated based
on road structures and camera pose, providing physical interpretation for
parameters of network output. The transformer models non-local interactions
with a self-attention mechanism to capture slender structures and global
context. The proposed method is validated on the TuSimple benchmark and shows
state-of-the-art accuracy with the most lightweight model size and fastest
speed. Additionally, our method shows excellent adaptability to a challenging
self-collected lane detection dataset, showing its powerful deployment
potential in real applications. Codes are available at
https://github.com/liuruijin17/LSTR.
