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 datasets MoLane and TuLane and the multi-target dataset MuLane. These datasets are built from three different domains, which cover diverse scenes and contain a total of 163K unique images, 118K of which are annotated. In addition we evaluate and report systematic baselines, including our own method, which builds upon Prototypical Cross-domain Self-supervised Learning. We find that false positive and false negative rates of the evaluated domain adaptation methods are high compared to those of fully supervised baselines. This affirms the need for benchmarks such as CARLANE to further strengthen research in Unsupervised Domain Adaptation for lane detection. CARLANE, all evaluated models and the corresponding implementations are publicly available at https://carlanebenchmark.github.io.

翻译：无监督域自适应通过将模型从标注源域迁移至未标注目标域，展现出缓解领域偏移的巨大潜力。尽管无监督域自适应已被应用于多种复杂视觉任务，但仅有少数研究聚焦于自动驾驶中的车道检测。这归因于公开数据集的匮乏。为促进该方向的研究，我们提出CARLANE——一个面向二维车道检测的三路仿真到真实域自适应基准。CARLANE包含单目标数据集MoLane和TuLane，以及多目标数据集MuLane。这些数据集源自三个不同领域，覆盖多样化场景，共计163K幅独特图像，其中118K幅已完成标注。此外，我们评估并报告了系统性基线结果，包括基于原型跨域自监督学习构建的自身方法。研究发现，与全监督基线相比，所评估的域自适应方法的假正率和假负率均较高。这进一步印证了如CARLANE此类基准的必要性，以推动车道检测领域无监督域自适应的深入研究。CARLANE、所有评估模型及相应实现代码已开源至https://carlanebenchmark.github.io。