  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.
