Deep Neural Network Based Roadwork Detection for Autonomous Driving
This work addresses a specific challenge for autonomous vehicles and traffic authorities in handling dynamic road construction sites, though it is incremental as it builds on existing methods like YOLO.
The paper tackles the problem of detecting and localizing road construction sites for autonomous driving by combining a YOLO neural network with LiDAR data, achieving a localization accuracy below 0.5 m in real-world evaluations.
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.