CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to multiple Real-World Domains. (arXiv:2206.08083v3 [cs.CV] UPDATED)

Unsupervised Domain Adaptation demonstrates great potential to mitigate
domain shifts by transferring models from labeled source domains to unlabeled
target domains. While Unsupervised Domain Adaptation has been applied to a wide
variety of complex vision tasks, only few works focus on lane detection for
autonomous driving. This can be attributed to the lack of publicly available
datasets. To facilitate research in these directions, we propose CARLANE, a
3-way sim-to-real domain adaptation benchmark for 2D lane detection. CARLANE
encompasses the single-target …

arxiv benchmark detection domain adaptation simulation unsupervised