  High-resolution road representations are a key factor for the success of
(highly) automated driving functions. These representations, for example,
high-definition (HD) maps, contain accurate information on a multitude of
factors, among others: road geometry, lane information, and traffic signs.
Through the growing complexity and functionality of automated driving
functions, also the requirements on testing and evaluation grow continuously.
This leads to an increasing interest in virtual test drives for evaluation
purposes. As roads play a crucial role in traffic flow, accurate real-world
representations are needed, especially when deriving realistic driving behavior
data. This paper proposes a novel approach to generate realistic road
representations based solely on point cloud information, independent of the
LiDAR sensor, mounting position, and without the need for odometry data,
multi-sensor fusion, machine learning, or highly-accurate calibration. As the
primary use case is simulation, we use the OpenDRIVE format for evaluation.
