Digital Twins and Cooperative Mobility
How can observations and models form useful, transferable representations of transportation systems?
We connect roadway sensing, learned representations, and simulation to develop transportation digital twins. Our work examines lane geometry and behavior, physical–virtual integration, and when cooperation between vehicles and infrastructure is useful.
Research directions
- Learning lane geometry and functional representations that transfer across scenes.
- Integrating roadside observations and connected-vehicle data with simulation.
- Assessing cooperative perception needs and studying human–AI interactions in shared environments.
Selected work
Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection
Preprint · 2025
A Digital Twin Framework for Physical-Virtual Integration in V2X-Enabled Connected Vehicle Corridors
IEEE Transactions on Intelligent Transportation Systems · 2025
Real-time identification of cooperative perception necessity in road traffic scenarios
Transportation Research Part C · 2026