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.

翻译：高精度道路表示是（高度）自动化驾驶功能成功的关键因素。这些表示，例如高清（HD）地图，包含多种因素的精确信息，包括：道路几何形状、车道信息和交通标志。随着自动驾驶功能的复杂性和功能性不断提高，测试和评估的要求也在持续增长。这导致人们对用于评估目的的虚拟测试驾驶越来越感兴趣。由于道路在交通流中起着关键作用，因此需要精确的真实世界表示，特别是在获取真实驾驶行为数据时。本文提出了一种新颖的方法，仅基于点云信息生成真实的道路表示，该方法独立于激光雷达传感器、安装位置，且无需里程计数据、多传感器融合、机器学习或高精度标定。由于主要应用场景是仿真，我们使用OpenDRIVE格式进行评估。