  Estimating the current scene and understanding the potential maneuvers are
essential capabilities of automated vehicles. Most approaches rely heavily on
the correctness of maps, but neglect the possibility of outdated information.
  We present an approach that is able to estimate lanes without relying on any
map prior. The estimation is based solely on the trajectories of other traffic
participants and is thereby able to incorporate complex environments. In
particular, we are able to estimate the scene in the presence of heavy traffic
and occlusions.
  The algorithm first estimates a coarse lane-level intersection model by
Markov chain Monte Carlo sampling and refines it later by aligning the lane
course with the measurements using a non-linear least squares formulation. We
model the lanes as 1D cubic B-splines and can achieve error rates of less than
10cm within real-time.
