Mobility & Driver Digital Twins

Driver-assistance systems are usually tuned for an average driver, while individual drivers differ in car following, lane changes, and stop-or-go decisions. This work asks how to keep synchronized models of drivers, vehicles, and traffic, and how to use them to predict and accommodate individual behavior. It develops digital-twin frameworks on a cloud–edge–device architecture and learns personalized driver models within them.

Main themes

  1. Mobility Digital Twin architecture

    A data-driven cloud–edge–device architecture with human, vehicle, and traffic digital twins, demonstrated through a personalized adaptive cruise control case study.

    Wang et al., IEEE IoT-J 2022

  2. Driver digital twins

    Online prediction of an individual driver's lane-change behavior and online recognition of distracted driving.

    Liao et al., IEEE IoT-J 2023 · Ma et al., IEEE T-IV 2024

  3. Personalized car following

    Gaussian-process models of individual car-following behavior learned from naturalistic driving data and combined with a predictive safety filter for personalized adaptive cruise control.

    Wang et al., IEEE T-ITS 2022 · Wang et al., IEEE ITSC 2021

  4. Personalization and trust

    A review of personalization in driving behavior covering datasets, modeling, and validation; prediction of personalized stop-or-go decisions in dilemma zones; and planning for automated vehicles that accounts for human trust.

    Liao et al., IEEE T-IV 2025 · Qin et al., IEEE RA-L 2025 · Sheng et al., ACM TCPS 2022

Systems and validation

  • Learning-based models of driver speed-tracking error for advisory speed assistance, tested with 17 drivers on a Unity game-engine driving simulator and further validated in a field implementation. (Wang et al., IEEE T-IV 2020)
  • Digital-twin simulation of connected and automated vehicles with the Unity game engine (Best Application Award). (Wang et al., IEEE DTPI 2021)

Representative publications

A curated selection. Citations above link to each paper; see the full publication list for all work.

IEEE IoT-J 2022

Mobility Digital Twin: Concept, Architecture, Case Study, and Future Challenges

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

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IEEE IoT-J 2023

Driver Digital Twin for Online Prediction of Personalized Lane-Change Behavior

X. Liao, X. Zhao, Z. Wang, Z. Zhao, K. Han, R. Gupta, M. Barth, and G. Wu

IEEE Internet of Things Journal, vol. 10, no. 15, pp. 13235–13246

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IEEE T-IV 2024

Driver Digital Twin for Online Recognition of Distracted Driving Behaviors

Y. Ma, R. Du, A. Abdelraouf, K. Han, R. Gupta, and Z. Wang

IEEE Transactions on Intelligent Vehicles, vol. 9, no. 2, pp. 3168–3180

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IEEE T-ITS 2022

Gaussian Process-Based Personalized Adaptive Cruise Control

Y. Wang, Z. Wang, K. Han, P. Tiwari, and D. Work

IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 21178–21189

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IEEE T-IV 2025

A Review of Personalization in Driving Behavior: Dataset, Modeling, and Validation

X. Liao, Z. Zhao, M. Barth, A. Abdelraouf, R. Gupta, K. Han, J. Ma, and G. Wu

IEEE Transactions on Intelligent Vehicles, vol. 10, no. 2, pp. 1241–1262

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IEEE RA-L 2025

Investigating Personalized Driving Behaviors in Dilemma Zones: Analysis and Prediction of Stop-or-Go Decisions

Z. Qin, S. Li, C. Wei, G. Wu, M. Barth, A. Abdelraouf, R. Gupta, and K. Han

IEEE Robotics and Automation Letters, vol. 10, no. 10, pp. 10035–10042

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Full publication list

Other research areas