Research question
How can shared digital representations help vehicles coordinate? Collaborative research connects vehicle, driver, and traffic models with vehicle-to-cloud communication, develops cooperative maneuver methods, and builds platforms for studying mixed traffic. Evaluations range from Unity–SUMO co-simulation to controlled passenger-vehicle field studies of ramp-merging advisories.
Vehicle and driver digital twins support ramp-merging advisories over cellular vehicle-to-cloud links. A related game-theoretic method studies mixed automated and human-driven traffic with Unity–SUMO co-simulation.
Liao et al., IEEE T-ITS 2022 · Liao et al., IEEE T-SMC 2022 · Wang et al., SAE JCAV 2019
Digital-twin-assisted methods extend cooperative-driving studies to non-signalized intersections and roundabouts. These investigate different maneuver geometries through simulation and co-simulation.
Mobility Digital Twin organizes human, vehicle, and traffic twins on a cloud–edge–device architecture. Unity and Unity–SUMO platforms support simulation and co-simulation of connected vehicles and mixed traffic.
Wang et al., IEEE IoT-J 2022 · Wang et al., IEEE DTPI 2021 · Zhao et al., SAE JCAV 2022
V2V sender identification associates messages with surrounding vehicles, while motion estimation accounts for communication delay and packet loss.
Lookup-table consensus studies longitudinal coordination, while a separate prototype-vehicle study examines platooning with communicated intent. Edge-assisted misbehavior detection investigates another vulnerability of platoon coordination.
Wang et al., ACC 2019 · Sharma et al., IEEE INFOCOM 2026 (poster) · Kan et al., IEEE VNC 2019
Augmented-reality assistance explores how connected-vehicle information can be presented to a driver.
Mobility Digital Twin's cloud-based driver-scoring demonstration in Mountain View, California.
Z. Wang et al., Mobility Digital Twin: Concept, Architecture, Case Study, and Future Challenges, IEEE IoT Journal (2022), Figure 3. DOI: 10.1109/JIOT.2022.3156028. © 2022 IEEE. Extracted from author manuscript; labels unchanged. Reused for the coauthor's personal academic use; no IEEE endorsement. Source for mobility-driver-scoring-field IEEE author-retained rights (not an open license)
Generic and personalized GCN-LSTM trajectory prediction compared on human-in-the-loop simulation data.
Amr Abdelraouf, Rohit Gupta, and Kyungtae Han (2023), Figure 4, arXiv:2308.07439v2. CC BY 4.0. Rasterized and cropped from the author preprint; data and labels unchanged. Source for personalized-trajectory-results CC BY 4.0
A curated selection. Citations above link to each paper; see the full publication list for all work.
X. Liao, Z. Wang, X. Zhao, K. Han, P. Tiwari, M. Barth, and G. Wu
IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 5, pp. 4490–4500
Z. Wang, R. Gupta, K. Han, H. Wang, A. Ganlath, N. Ammar, and P. Tiwari
IEEE Internet of Things Journal, vol. 9, no. 18, pp. 17452–17467
X. Liao, X. Zhao, Z. Wang, K. Han, P. Tiwari, M. Barth, and G. Wu
IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 9, pp. 5746–5757
Z. Wang, K. Han, and P. Tiwari
IEEE Transactions on Intelligent Vehicles, vol. 7, no. 2, pp. 198--209
X. Zhao, X. Liao, G. Wu, M. Barth, Z. Wang, K. Han, and P. Tiwari
SAE International Journal of Connected and Automated Vehicles, vol. 5, no. 4, pp. 313--326
S. Sharma, A. Moradipari, S. Avedisov, M. Nour, S. Mishra, K. Han, A. Abdelraouf, T. Shimizu, and O. Altintas
IEEE INFOCOM 2026, Tokyo, Japan