  Despite recent advances in lane detection methods, scenarios with limited- or
no-visual-clue of lanes due to factors such as lighting conditions and
occlusion remain challenging and crucial for automated driving. Moreover,
current lane representations require complex post-processing and struggle with
specific instances. Inspired by the DETR architecture, we propose LDTR, a
transformer-based model to address these issues. Lanes are modeled with a novel
anchor-chain, regarding a lane as a whole from the beginning, which enables
LDTR to handle special lanes inherently. To enhance lane instance perception,
LDTR incorporates a novel multi-referenced deformable attention module to
distribute attention around the object. Additionally, LDTR incorporates two
line IoU algorithms to improve convergence efficiency and employs a Gaussian
heatmap auxiliary branch to enhance model representation capability during
training. To evaluate lane detection models, we rely on Frechet distance,
parameterized F1-score, and additional synthetic metrics. Experimental results
demonstrate that LDTR achieves state-of-the-art performance on well-known
datasets.
