Next Generation Mobility Systems

Digital Twins

Our gruop develops multi-fidelity, autonomous, and self-sustaining digital twins that support time-sensitive decisions. Multi-resolution models provide scalability from component to system level. The twins use statistical inference to detect knowledge gaps in real time and then plan and dispatch sensing or control actions to close them, optimally allocating assets.

Key publications Example projects

Technical approach

Bayesian Modeling, Sequential decison-making, Simulation.

Example projects

SCC-IRG Track 2: Digital Twin City for Age-friendly Communities - Crowd-biosensing of Environmental Distress for Older Adults
Enhancing Military Digital Twins: Leveraging Dynamic Data-Driven Application Systems for Complex Operational Scenarios
Digital Twin for Construction and Logistics for Lego-Inspired Construction

Key publications

Lee, D., Lee, S., Masoud, N., Krishnan, M. S., & Li, V. C. (2022). Digital twin-driven deep reinforcement learning for adaptive task allocation in robotic construction. Advanced Engineering Informatics, 53, 101710.
Lee, D., Lee, S., Masoud, N., Krishnan, M. S., & Li, V. C. (2021). Integrated digital twin and blockchain framework to support accountable information sharing in construction projects. Automation in Construction, 127, 103688.