2025
Unmanned Road Damage Restoration Technology
Building the world's first unmanned road pavement maintenance system combining AI robotics and advanced materials — RISE Industry-Academia Joint Research with KAIST, SNU, and Rovoroad.
Overview
Launched in July 2025 as part of the RISE (Regional Innovation System & Education) program, this 3-year project aims to build the world’s first unmanned road pavement maintenance system that is fast, economical, and safe — combining AI robotics technology, lifecycle decision-making, and advanced materials.
Background
- 94% of domestic roads are over 40 years old since construction
- Heavy rain and snowfall increasing; days with temperature variation exceeding 10°C rose 40% over the past decade
- Road repair budgets doubled over 8 years; public complaints increased 10-fold over 5 years
- Jeju’s coastal climate and tourism characteristics mean road damage directly affects the regional image
Core Technologies
- Real-time road condition monitoring: AI-based automatic road damage detection and classification using edge AI on roadside devices and vehicle-mounted cameras
- Unmanned road damage restoration robot: Fully autonomous restoration process with no worker intervention, developed with KAIST’s robot control expertise
- Eco-friendly repair materials: High-strength, waterproof, high-durability alternative materials suited to Jeju’s climate, developed with Seoul National University
- Integrated control system: Restoration priority decision-making based on tourism and logistics vehicle movement pattern analysis
- Demonstration-based commercialization: Target 90%+ reduction in road damage-derived problems
Participating Institutions
| Institution | Role |
|---|---|
| Cheju Halla University (Lead) | Overall research management, AI system development |
| KAIST | Robot control and autonomous driving technology |
| Seoul National University | Road materials and advanced materials research |
| Rovoroad (Corporation) | Industry partner, demonstration and commercialization |
Student Researchers
Four student researchers from the Department of AI participate in AI road damage detection system development — building image analysis models, control system data pipelines, and conducting demonstration data collection and performance evaluation.
Project Information
- Research Period: July 2025 – February 2028 (3 years)
- Total R&D Budget: 900 million KRW
- Principal Investigator: Professor Young Joon Lee, Cheju Halla University