YOLinO++: Single-Shot Estimation of Generic Polylines for Mapless
  Automated Diving
Annika Meyer Christoph Stiller
In automated driving, highly accurate maps are commonly used to support and
complement perception. These maps are costly to create and quickly become
outdated as the traffic world is permanently changing. In order to support or
replace the map of an automated system with detections from sensor data, a
perception module must be able to detect the map features. We propose a neural
network that follows the one shot philosophy of YOLO but is designed for
detection of 1D structures in images, such as lane boundaries.
  We extend previous ideas by a midpoint based line representation and anchor
definitions. This representation can be used to describe lane borders,
markings, but also implicit features such as centerlines of lanes. The broad
applicability of the approach is shown with the detection performance on lane
centerlines, lane borders as well as the markings both on highways and in urban
areas.
  Versatile lane boundaries are detected and can be inherently classified as
dashed or solid lines, curb, road boundaries, or implicit delimitation.