Jiaming Zhong

01 / RESEARCH

Learning brings adaptation and contextual understanding; model-based priors ground it in physics, explicit constraints, and feedback. I bring these strengths together across perception, decision-making, and action.

Learning-based planning and control for articulated heavy-duty and off-road vehicles

Improving heavy-vehicle safety under challenging road and weather conditions.

SafeTrucks project ↗

DriveLLM: Explainable Driving Decisions

Language-model reasoning with rule-based safety checks for explainable driving decisions, evaluated through WATonobus case studies.

Paper · IEEE T-IV ↗Project ↗

Learning-based multi-agent collaboration

Cooperating agents and learned negotiation initialization for plug-and-play vehicle control and faster multi-objective MPC.

Paper · IEEE T-ITS 2025 ↗Paper · ITSC 2024 ↗Lab project ↗

Learning-based MPC

Online Gaussian-process dynamics learning with stochastic MPC, tested on real vehicles under changing and low-friction conditions.

Paper · IEEE T-ITS 2024 ↗Lab project ↗PhD thesis ↗

F1TENTH: World Models for Agile Racing

Ongoing exploration of multimodal world models for agile planning and control on an autonomous racing platform with vision and LiDAR.

Platform ↗

XLeRobot: Generative Mobile Manipulation

Ongoing work on generative mobile manipulation with action-chunking policies, world-action models, and vision-language-action models.

Platform ↗

02 / SELECTED PUBLICATIONS

1. 2025

   Multi-objective agent-based model predictive controller for plug-and-play vehicle control

   J. Zhong^†, L. Khoshnevisan, S. Huang, M. Pirani, Y. V. Pant, A. Khajepour

   IEEE Transactions on Intelligent Transportation Systems, 26(12), 22818–22829

2. 2024

   Learning agent-based model predictive control for holistic vehicle performance

   J. Zhong^†, R. V. Mehrizi, M. Pirani, C. Yu, A. Kasaiezadeh, Y. V. Pant, A. Khajepour

   IEEE Transactions on Intelligent Transportation Systems, 25(11), 17482–17492

3. 2024

   DriveLLM: Charting the path toward full autonomous driving with large language models

   Y. Cui, S. Huang, J. Zhong^*, Z. Liu, Y. Wang, C. Sun, B. Li, X. Wang, A. Khajepour

   IEEE Transactions on Intelligent Vehicles, 9(1), 1450–1464

4. 2025

   A review on vehicle steer-by-wire control systems

   Y. Lu, J. Zhong^‡, L. Khoshnevisan, C. Sun, A. Khajepour

   Vehicle System Dynamics, 1–33

5. 2026

   DriveLegal: Toward Legally Compliant Driving via Trustworthy Hybrid Retrieval-Augmented LLMs

   S. Huang, C. Sun, M. Ning, Y. Yang, C. Ma, J. Zhong, K. Shu, F. Shi, A. Khajepour

   Expert Systems with Applications, 314, 131593

6. 2025

   DriveSOTIF: Advancing SOTIF Through Multimodal Large Language Models

   S. Huang, F. Shi, C. Sun, J. Zhong, M. Ning, Y. Yang, Y. Lu, H. Wang, A. Khajepour

   IEEE Transactions on Vehicular Technology, 1–14

7. 2025

   Social predictive intelligent driver model for autonomous driving simulation

   Z. Deng, W. Hu, T. Huang, C. Sun, J. Zhong, A. Khajepour

   Automotive Innovation, 8(1), 1–12

8. 2025

   Practical solution for attenuating industrial heavy vehicle vibration: A new gain-adaptive coordinated suspension control system

   Y. Lu, R. Zhen, Y. Liu, J. Zhong, C. Sun, Y. Huang, A. Khajepour

   Control Engineering Practice, 154, 106125

9. 2024

   A data-driven distributed control scheme: Learning multi-objective agent-based MPC for path-tracking

   J. Zhong^†, R. V. Mehrizi, Y. V. Pant, A. Khajepour

   IEEE International Conference on Intelligent Transportation Systems (ITSC), 2999–3004