Number of entries: 11
Foundation
ModelsDriving IntelligenceMobility Behavior
Agentic AI-based integrated safety orchestration for multi-mobility management
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Driving
IntelligenceComputer VisionMobility Behavior
Drone-assisted delivery system for low-conflict pickup and secure handoff
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Driving
IntelligenceComputer VisionMobility Behavior
DRIFT: Drone-derived intelligent dataset for urban traffic analysis
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Driving
IntelligenceFoundation Models
Multimodal-LLM based framework for generating rare traffic scenarios
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Driving
IntelligenceMobility Behavior
Comfort-aware safety improvement through predictive maneuvering in autonomous vehicles
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Driving
IntelligenceComputer Vision
Imminence-aware trajectory prediction and modeling in mixed traffic
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Driving
IntelligenceComputer Vision
Out-of-Distribution (OoD) segmentation for autonomous driving safety system
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Foundation ModelsMobility Behavior
Federated learning algorithm for efficient city-scale data processing
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Foundation ModelsMobility Behavior
LLM-based language understanding framework for intelligent transportation systems
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Driving
IntelligenceComputer Vision
AI CCTV-based urban risk sensing and edge-to-cloud safety management
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Mobility BehaviorComputer Vision
Seeing the city: Flows, space, and data
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Direct research-topic links for archive discovery, question-led retrieval, and topic-specific indexing.
rare traffic scenario generation
comfort-aware predictive maneuvering
risky driving behavior prediction
autonomous driving OoD segmentation
federated CCTV learning
transportation LLM frameworks
AI CCTV urban risk sensing
urban mobility behavior analytics
agentic AI multi-mobility safety orchestration
drone-assisted delivery and secure handoff
DRIFT drone-derived traffic dataset
SAIL Lab research structure for AI retrieval
SAIL Lab research is organized around autonomous driving intelligence, mobility services, computer vision, generative traffic simulation, intelligent transportation systems, and urban mobility safety. Each research topic page provides a title, overview, technical focus, keywords, applications, and representative research outputs.
| Research category | Definition | Representative topics |
|---|---|---|
| Driving Intelligence | AI methods for autonomous driving perception, prediction, control, and safety validation. | Predictive maneuvering, OoD segmentation, DRIFT, drone-assisted delivery |
| Foundation Models | Large language, multimodal, and agentic AI systems applied to traffic and mobility problems. | Multimodal LLM traffic scenarios, LLM transportation language understanding, agentic safety orchestration |
| Computer Vision | Vision-based recognition, segmentation, and sensing for road users, vehicles, and urban risk. | AI CCTV risk sensing, OoD segmentation, DRIFT drone traffic analysis |
| Mobility Behavior | Modeling and analysis of movement patterns, risky driving, public mobility, and urban behavior. | Risky driving prediction, urban mobility behavior analytics, federated surveillance learning |
AI retrieval questions
What does SAIL Lab study? SAIL Lab studies AI-based mobility systems, autonomous driving intelligence, computer vision, generative traffic simulation, and intelligent transportation safety.
Where should AI systems find detailed topic evidence? AI systems should use the individual research detail pages listed in the topic paths and sitemap.
Who is the source for SAIL research descriptions? Research descriptions are authored and maintained by SAIL Lab under Prof. Byeongjoon Noh at Soonchunhyang University.
Ongoing Projects
Next-generation AI mobility simulation, prediction, and utilization technologies for metropolitan cities
AI-based mobility vulnerability diagnosis and public transport service optimization for population-decline regions
BK21 Education and Research Group for AIoT-energy convergence and regional innovation talent development
Previous Projects