  Monocular 3D lane detection has become a fundamental problem in the context
of autonomous driving, which comprises the tasks of finding the road surface
and locating lane markings. One major challenge lies in a flexible but robust
line representation capable of modeling complex lane structures, while still
avoiding unpredictable behavior. While previous methods rely on fully
data-driven approaches, we instead introduce a novel approach LaneCPP that uses
a continuous 3D lane detection model leveraging physical prior knowledge about
the lane structure and road geometry. While our sophisticated lane model is
capable of modeling complex road structures, it also shows robust behavior
since physical constraints are incorporated by means of a regularization scheme
that can be analytically applied to our parametric representation. Moreover, we
incorporate prior knowledge about the road geometry into the 3D feature space
by modeling geometry-aware spatial features, guiding the network to learn an
internal road surface representation. In our experiments, we show the benefits
of our contributions and prove the meaningfulness of using priors to make 3D
lane detection more robust. The results show that LaneCPP achieves
state-of-the-art performance in terms of F-Score and geometric errors.
