Road construction sites create major challenges for both autonomous vehicles and human drivers due to their highly dynamic and heterogeneous nature. This paper presents a real-time system that detects and localizes roadworks by combining a YOLO neural network with LiDAR data. The system identifies individual roadwork objects while driving, merges them into coherent construction sites and records their outlines in world coordinates. The model training was based on an adapted US dataset and a new dataset collected from test drives with a prototype vehicle in Berlin, Germany. Evaluations on real-world road construction sites showed a localization accuracy below 0.5 m. The system can support traffic authorities with up-to-date roadwork data and could enable autonomous vehicles to navigate construction sites more safely in the future.

翻译：道路施工场地因其高度动态和异质性，给自动驾驶车辆和人类驾驶员均带来重大挑战。本文提出一种实时检测与定位道路施工区域的系统，该系统通过结合YOLO神经网络与LiDAR数据，在行驶过程中识别单个道路施工物体，将其整合为连贯的施工场地，并在世界坐标系中记录其轮廓。模型训练基于经过适配的美国数据集以及一辆原型车在德国柏林测试行驶中收集的新数据集。在真实道路施工场地上的评估显示，定位精度低于0.5米。该系统可向交通管理部门提供最新的道路施工数据，未来也有望使自动驾驶车辆更安全地穿越施工区域。