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

Knowledge of lane topology is a core problem in au-tonomous driving. Aerial imagery can provide high res-olution, quickly updatable lane source data but detecting lanes from such data has so far been an expensive man-ual process or, where automated solutions exist, undriv-able and requiring of downstream processing. We pro-pose 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