融合激光雷达与YOLO实时检测道路施工，定位精度超0.5米。
Deep Neural Network Based Roadwork Detection for Autonomous Driving
- 用YOLO结合激光雷达数据识别施工物体并合并成完整区域。
- 在真实场景下定位误差低于0.5米，支持实时导航。
- 适合自动驾驶车辆与交通管理部门使用。
道路施工因高度动态和异构性，对自动驾驶车辆和人类驾驶员均构成重大挑战。本文提出一种实时系统，通过结合YOLO神经网络与激光雷达数据，实现道路施工的检测与定位。该系统可在行驶中识别单个施工对象，将其合并为连贯的施工区域，并以世界坐标记录其轮廓。模型训练基于一个改良的美国数据集及在德国柏林测试车采集的新数据集。在真实道路施工场景下的评估显示，定位精度低于0.5米。该系统可为交通管理部门提供实时更新的施工数据，未来有望使自动驾驶车辆更安全地通过施工区域。
原文摘要 · Abstract (English)
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.
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