A Digital Twin Framework for Traffic-Aware UAV Pavement Monitoring in Open-Traffic Conditions
arXiv:2606.20742v2 Announce Type: replace-cross Abstract: UAV-based pavement inspection can reduce the cost and risk of road-surface monitoring, but real-world deployment remains difficult when traffic, pedestrians, and temporary occlusions affect defect visibility. This paper presents a Unity-based digital twin framework for traffic-aware UAV pavement monitoring in open-traffic conditions. The proposed environment integrates procedurally generated road defects, dynamic traffic agents, autonomous UAV navigation, and a multitask YOLOv8n perception module for detecting road defects, pedestrians,
The increasing maturity of drone technology, AI perception models, and digital twin frameworks converges to enable more robust and autonomous infrastructure monitoring solutions.
This development allows for safer, more cost-effective, and highly efficient inspection of critical infrastructure like road networks, reducing manual labor risks and improving maintenance cycles.
The ability to accurately monitor pavement defects in real-time and open-traffic conditions significantly enhances predictive maintenance capabilities and reduces reliance on dangerous human inspections.
- · Infrastructure maintenance companies
- · UAV manufacturers
- · AI software developers
- · Government transportation departments
- · Traditional manual inspection services
Widespread adoption of autonomous drone systems for infrastructure monitoring.
Improved road safety and reduced long-term infrastructure repair costs due to proactive intervention.
Extension of similar digital twin and drone monitoring frameworks to other complex infrastructure, like bridges, railways, and pipelines.
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Read at arXiv cs.AI