  Modern cars are incorporating an increasing number of driver assist features,
among which automatic lane keeping. The latter allows the car to properly
position itself within the road lanes, which is also crucial for any subsequent
lane departure or trajectory planning decision in fully autonomous cars.
Traditional lane detection methods rely on a combination of highly-specialized,
hand-crafted features and heuristics, usually followed by post-processing
techniques, that are computationally expensive and prone to scalability due to
road scene variations. More recent approaches leverage deep learning models,
trained for pixel-wise lane segmentation, even when no markings are present in
the image due to their big receptive field. Despite their advantages, these
methods are limited to detecting a pre-defined, fixed number of lanes, e.g.
ego-lanes, and can not cope with lane changes. In this paper, we go beyond the
aforementioned limitations and propose to cast the lane detection problem as an
instance segmentation problem - in which each lane forms its own instance -
that can be trained end-to-end. To parametrize the segmented lane instances
before fitting the lane, we further propose to apply a learned perspective
transformation, conditioned on the image, in contrast to a fixed "bird's-eye
view" transformation. By doing so, we ensure a lane fitting which is robust
against road plane changes, unlike existing approaches that rely on a fixed,
pre-defined transformation. In summary, we propose a fast lane detection
algorithm, running at 50 fps, which can handle a variable number of lanes and
cope with lane changes. We verify our method on the tuSimple dataset and
achieve competitive results.
