  Recently, lane detection has made great progress with the rapid development
of deep neural networks and autonomous driving. However, there exist three
mainly problems including characterizing lanes, modeling the structural
relationship between scenes and lanes, and supporting more attributes (e.g.,
instance and type) of lanes. In this paper, we propose a novel structure guided
framework to solve these problems simultaneously. In the framework, we first
introduce a new lane representation to characterize each instance. Then a
topdown vanishing point guided anchoring mechanism is proposed to produce
intensive anchors, which efficiently capture various lanes. Next, multi-level
structural constraints are used to improve the perception of lanes. In the
process, pixel-level perception with binary segmentation is introduced to
promote features around anchors and restore lane details from bottom up, a
lane-level relation is put forward to model structures (i.e., parallel) around
lanes, and an image-level attention is used to adaptively attend different
regions of the image from the perspective of scenes. With the help of
structural guidance, anchors are effectively classified and regressed to obtain
precise locations and shapes. Extensive experiments on public benchmark
datasets show that the proposed approach outperforms state-of-the-art methods
with 117 FPS on a single GPU.
