  Lane detection is a fundamental task in autonomous driving, and has achieved
great progress as deep learning emerges. Previous anchor-based methods often
design dense anchors, which highly depend on the training dataset and remain
fixed during inference. We analyze that dense anchors are not necessary for
lane detection, and propose a transformer-based lane detection framework based
on a sparse anchor mechanism. To this end, we generate sparse anchors with
position-aware lane queries and angle queries instead of traditional explicit
anchors. We adopt Horizontal Perceptual Attention (HPA) to aggregate the lane
features along the horizontal direction, and adopt Lane-Angle Cross Attention
(LACA) to perform interactions between lane queries and angle queries. We also
propose Lane Perceptual Attention (LPA) based on deformable cross attention to
further refine the lane predictions. Our method, named Sparse Laneformer, is
easy-to-implement and end-to-end trainable. Extensive experiments demonstrate
that Sparse Laneformer performs favorably against the state-of-the-art methods,
e.g., surpassing Laneformer by 3.0% F1 score and O2SFormer by 0.7% F1 score
with fewer MACs on CULane with the same ResNet-34 backbone.
