Deep Neural Network Based Roadwork Detection for Autonomous Driving

arXiv cs.RO / 4/3/2026

💬 OpinionSignals & Early TrendsIdeas & Deep AnalysisModels & Research

共有:

Key Points

The paper proposes a real-time roadwork detection and localization system for autonomous driving by fusing a YOLO-based neural network with LiDAR sensing data.
It detects individual roadwork objects during driving, then merges detections into coherent construction-site maps and records their outlines in world coordinates.
Training uses an adapted U.S. dataset plus a newly collected dataset from test drives using a prototype vehicle in Berlin, Germany.
Real-world evaluations on active construction sites report localization accuracy better than 0.5 meters, indicating strong practical viability.
The authors argue the approach could provide traffic authorities with up-to-date roadwork information and help autonomous vehicles navigate construction zones more safely.

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.