  Lane detection is one of the fundamental modules in self-driving. In this
paper we employ a transformer-only method for lane detection, thus it could
benefit from the blooming development of fully vision transformer and achieve
the state-of-the-art (SOTA) performance on both CULane and TuSimple benchmarks,
by fine-tuning the weight fully pre-trained on large datasets. More
importantly, this paper proposes a novel and general framework called
PriorLane, which is used to enhance the segmentation performance of the fully
vision transformer by introducing the low-cost local prior knowledge.
Specifically, PriorLane utilizes an encoder-only transformer to fuse the
feature extracted by a pre-trained segmentation model with prior knowledge
embeddings. Note that a Knowledge Embedding Alignment (KEA) module is adapted
to enhance the fusion performance by aligning the knowledge embedding.
Extensive experiments on our Zjlab dataset show that PriorLane outperforms SOTA
lane detection methods by a 2.82% mIoU when prior knowledge is employed, and
the code will be released at: https://github.com/vincentqqb/PriorLane.
