Deep Neural Network Based Roadwork Detection for Autonomous Driving

TL;DR

This paper introduces a real-time deep learning system combining YOLO and LiDAR to detect and localize roadworks, aiding autonomous driving and traffic management.

Contribution

It presents a novel integrated system for real-time roadwork detection and localization using neural networks and sensor data, tested in real-world conditions.

Findings

Localization accuracy below 0.5 meters on real sites.

System successfully detects and merges roadwork objects into coherent sites.

Supports safer navigation and traffic management.

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.