Improving Lane Detection Generalization: A Novel Framework using HD Maps
  for Boosting Diversity
Daeun Lee Minhyeok Heo Jiwon Kim
Lane detection is a vital task for vehicles to navigate and localize their
position on the road. To ensure reliable results, lane detection algorithms
must have robust generalization performance in various road environments.
However, despite the significant performance improvement of deep learning-based
lane detection algorithms, their generalization performance in response to
changes in road environments still falls short of expectations. In this paper,
we present a novel framework for single-source domain generalization (SSDG) in
lane detection. By decomposing data into lane structures and surroundings, we
enhance diversity using High-Definition (HD) maps and generative models. Rather
than expanding data volume, we strategically select a core subset of data,
maximizing diversity and optimizing performance. Our extensive experiments
demonstrate that our framework enhances the generalization performance of lane
detection, comparable to the domain adaptation-based method.