Lane Segmentation Refinement with Diffusion Models
<|reference_start|>Lane Segmentation Refinement with Diffusion Models: 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.<|reference_end|>
@article{ruiz2024lane,
    title={Lane Segmentation Refinement with Diffusion Models},
    author={Antonio Ruiz, Andrew Melnik, Dong Wang and Helge Ritter},
    journal={arXiv preprint arXiv:2405.00620},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.00620},
    primaryClass={cs.CV}
}