Deep Neural Network Based Roadwork Detection for Autonomous Driving
2026-04-02 • Robotics
RoboticsComputer Vision and Pattern Recognition
AI summaryⓘ
The authors developed a system that helps detect and locate road construction areas in real time for both self-driving and human-driven cars. Their system uses a type of artificial intelligence called YOLO combined with 3D laser data (LiDAR) to find and map roadwork objects as the vehicle moves. They trained the model using data from the US and new data collected in Berlin. Tests showed the system can pinpoint construction sites with less than half a meter of error. This could help keep traffic info up to date and make driving through roadworks safer.
YOLO neural networkLiDARroad construction detectionobject localizationreal-time systemautonomous vehiclesdataset trainingtraffic management3D mapping
Authors
Sebastian Wullrich, Nicolai Steinke, Daniel Goehring
Abstract
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.