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.

翻译：车道检测是车辆在道路上导航和定位自身位置的关键任务。为确保结果的可靠性，车道检测算法必须在各种道路环境下具备稳健的泛化性能。然而，尽管基于深度学习的车道检测算法取得了显著的性能提升，其应对道路环境变化的泛化能力仍未达到预期。本文提出了一种用于车道检测中单源域泛化（SSDG）的新颖框架。通过将数据分解为车道结构和周围环境，我们利用高精地图（HD maps）和生成模型增强多样性。该方法并非扩大数据量，而是战略性地选择核心数据子集，以最大化多样性并优化性能。大量实验表明，我们的框架提升了车道检测的泛化性能，效果可与基于域适应的方法相媲美。