  This study presents an approach to lane detection involving the prediction of
binary segmentation masks and per-pixel affinity fields. These affinity fields,
along with the binary masks, can then be used to cluster lane pixels
horizontally and vertically into corresponding lane instances in a
post-processing step. This clustering is achieved through a simple row-by-row
decoding process with little overhead; such an approach allows LaneAF to detect
a variable number of lanes without assuming a fixed or maximum number of lanes.
Moreover, this form of clustering is more interpretable in comparison to
previous visual clustering approaches, and can be analyzed to identify and
correct sources of error. Qualitative and quantitative results obtained on
popular lane detection datasets demonstrate the model's ability to detect and
cluster lanes effectively and robustly. Our proposed approach sets a new
state-of-the-art on the challenging CULane dataset and the recently introduced
Unsupervised LLAMAS dataset.
