Semi-Local 3D Lane Detection and Uncertainty Estimation
This addresses the problem of accurate and generalizable 3D lane detection for autonomous driving systems, with incremental improvements in handling complex topologies and adding uncertainty estimation.
The paper tackles 3D lane detection from camera images by proposing a semi-local, BEV tile representation that breaks lanes into segments, combining parametric modeling and deep embeddings to cluster segments into full lanes, achieving state-of-the-art results in experiments.
We propose a novel camera-based DNN method for 3D lane detection with uncertainty estimation. Our method is based on a semi-local, BEV, tile representation that breaks down lanes into simple lane segments. It combines learning a parametric model for the segments along with a deep feature embedding that is then used to cluster segment together into full lanes. This combination allows our method to generalize to complex lane topologies, curvatures and surface geometries. Additionally, our method is the first to output a learning based uncertainty estimation for the lane detection task. The efficacy of our method is demonstrated in extensive experiments achieving state-of-the-art results for camera-based 3D lane detection, while also showing our ability to generalize to complex topologies, curvatures and road geometries as well as to different cameras. We also demonstrate how our uncertainty estimation aligns with the empirical error statistics indicating that it is well calibrated and truly reflects the detection noise.