Bezier Everywhere All at Once: Learning Drivable Lanes as Bezier Graphs

Hugh Blayney, Hanlin Tian, Hamish Scott, Nils Goldbeck, Chess Stetson, Panagiotis Angeloudis; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 15365-15374

Abstract

Knowledge of lane topology is a core problem in autonomous driving. Aerial imagery can provide high resolution quickly updatable lane source data but detecting lanes from such data has so far been an expensive manual process or where automated solutions exist undrivable and requiring of downstream processing. We propose a method for large-scale lane topology extraction from aerial imagery while ensuring that the resulting lanes are realistic and drivable by introducing a novel Bezier Graph shared parameterisation of Bezier curves. We develop a transformer-based model to predict these Bezier Graphs from input aerial images demonstrating competitive results on the UrbanLaneGraph dataset. We demonstrate that our method generates realistic lane graphs which require both minimal input and minimal downstream processing. We make our code publicly available at https://github.com/driskai/BGFormer

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[bibtex]
@InProceedings{Blayney_2024_CVPR, author = {Blayney, Hugh and Tian, Hanlin and Scott, Hamish and Goldbeck, Nils and Stetson, Chess and Angeloudis, Panagiotis}, title = {Bezier Everywhere All at Once: Learning Drivable Lanes as Bezier Graphs}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {15365-15374} }