  Lane detection is a vital task for vehicles to navigate and localize their
position on the road. To ensure reliable driving, lane detection models must
have robust generalization performance in various road environments. However,
despite the advanced performance in the trained domain, their generalization
performance still falls short of expectations due to the domain discrepancy. To
bridge this gap, we propose a novel generative framework using HD Maps for
Single-Source Domain Generalization (SSDG) in lane detection. We first generate
numerous front-view images from lane markings of HD Maps. Next, we
strategically select a core subset among the generated images using (i) lane
structure and (ii) road surrounding criteria to maximize their diversity. In
the end, utilizing this core set, we train lane detection models to boost their
generalization performance. We validate that our generative framework from HD
Maps outperforms the Domain Adaptation model MLDA with +3.01%p accuracy
improvement, even though we do not access the target domain images.
