Driving and Control Strategies for Automated Vehicles
Develops driving and control strategies that let automated vehicles move safely and efficiently in mixed traffic, from lane changes to cooperation with traffic signals.
Learn more →MoveLab · Ajou University
Designing, validating, and enhancing how mobility operates.
MoveLab at Ajou University studies automated driving and smart mobility from a transportation perspective. The lab develops driving strategies for automated vehicles, builds and validates test scenarios, analyzes traffic safety with AI, and estimates traffic conditions from in-vehicle sensors.
Research Areas
Test Scenario Generation and Validation
Builds test scenarios from crash records and real-world driving data, and uses them to validate automated vehicles in simulation and on real roads.
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AI-Based Traffic Safety Analysis
Applies deep learning and large language models to crash and traffic data to predict crash risk, identify high-risk road sections, and support countermeasures.
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Traffic Measures from In-Vehicle Sensors
Estimates traffic conditions such as density and speed from sensors already on board vehicles, extending traffic monitoring beyond fixed roadside detectors.
Learn more →News, Updates
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Eunho Lee, M.S. Student, received the Excellent Master's Scholarship in Science & Engineering from the Ministry of Science and ICT
Sep. 2026
Movelab attended and presented at IEEE ITSC 2026 in Naples, Italy
Sep. 2026
Degree Conferral Ceremony 2026, Movelab's Seunghee Lee received Bachelor's Degree
Aug. 2026
A-Movelab at the 2026 Gangneung ITS World Congress Youth Fellowship Workshop