Abstract
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.
Abstract (translated)
道路图是构建高清晰度（HD）地图的关键组件，对于自动驾驶或导航规划等下游任务至关重要。之前，He等人（2022）利用基于分割的方法从无人机影像中提取道路级图。然而，分割网络很难实现完美的分割掩码，导致道路级图提取不准确。我们探讨了使用基于分割的改进方法，并将其与扩散概率模型（DPM）组件相结合，以进一步改进该方法。这种组合进一步提高了无向图中的GEO F1和TOPO F1分数，这些分数是衡量道路图质量的关键指标。我们在公开可用数据集上进行实验，证明我们的方法超越了以前的方法，特别是在增强道路图的连通性方面，根据TOPO F1得分。此外，我们对我们方法的个人组件进行了消融研究，以了解它们的贡献并评估其效果。
URL
https://arxiv.org/abs/2405.00620
