The lane graph is a key component for building high-definition (HD) maps and crucial for downstream tasks such as autonomous driving or navigation planning. Previously, He et al. (2022) explored the extraction of the lane-level graph from aerial imagery utilizing a segmentation based approach. However, segmentation networks struggle to achieve perfect segmentation masks resulting in inaccurate lane graph extraction. We explore additional enhancements to refine this segmentation-based approach and extend it with a diffusion probabilistic model (DPM) component. This combination further improves the GEO F1 and TOPO F1 scores, which are crucial indicators of the quality of a lane graph, in the undirected graph in non-intersection areas. We conduct experiments on a publicly available dataset, demonstrating that our method outperforms the previous approach, particularly in enhancing the connectivity of such a graph, as measured by the TOPO F1 score. Moreover, we perform ablation studies on the individual components of our method to understand their contribution and evaluate their effectiveness.

翻译：车道图是构建高精（HD）地图的关键组成部分，对自动驾驶或导航规划等下游任务至关重要。此前，He等人（2022）探索了利用基于分割的方法从航拍图像中提取车道级图结构。然而，分割网络难以生成完美的分割掩码，导致车道图提取不准确。我们探索了额外增强措施来优化这种基于分割的方法，并引入扩散概率模型（DPM）组件进行扩展。这种组合进一步提升了GEO F1和TOPO F1分数——这两个指标是衡量非交叉区域无向车道图质量的关键指标。我们在公开数据集上进行了实验，结果表明我们的方法优于先前方法，尤其在通过TOPO F1分数衡量的图连通性方面表现突出。此外，我们对方法各组件进行了消融研究，以理解其贡献并评估有效性。