Publications | Zhengyang WAN

2026

arXiv
Featured

Sim2Real-AD: A Modular Sim-to-Real Framework for Deploying VLM-Guided Reinforcement Learning in Real-World Autonomous Driving

Zilin Huang, Zhengyang Wan, Zihao Sheng, and

Sim2Real-AD is a modular framework for transferring CARLA-trained, VLM-guided reinforcement-learning policies to full-scale vehicles without real-world RL training data. It supports zero-shot real-world deployment despite simulator-specific observation and action-semantics mismatches.

arXiv preprint arXiv:2604.03497 · 2026

Paper

Scholar
@article{huang2026sim2real, title = {Sim2Real-AD: A Modular Sim-to-Real Framework for Deploying VLM-Guided Reinforcement Learning in Real-World Autonomous Driving}, author = {Huang, Zilin and Wan, Zhengyang and Sheng, Zihao and Wang, Boyue and You, Junwei and Leng, Yue and Chen✉, Sikai}, journal = {arXiv preprint arXiv:2604.03497}, year = {2026}, priority = {0}, }
arXiv
Featured

DriveVLM-RL: Neuroscience-Inspired Reinforcement Learning with Vision-Language Models for Safe and Deployable Autonomous Driving

Zilin Huang, Zihao Sheng, Zhengyang Wan, and

DriveVLM-RL combines a CLIP-based static safety pathway with attention-gated multi-frame VLM reasoning to learn semantic rewards for autonomous driving. Its asynchronous training pipeline removes all VLM components at deployment while improving collision avoidance, task success, and generalization in CARLA.

arXiv preprint arXiv:2603.18315 · 2026

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Scholar 4
@article{huang2026drivevlmrl, title = {DriveVLM-RL: Neuroscience-Inspired Reinforcement Learning with Vision-Language Models for Safe and Deployable Autonomous Driving}, author = {Huang, Zilin and Sheng, Zihao and Wan, Zhengyang and Qu, Yansong and You, Junwei and Jiang, Sicong and Chen✉, Sikai}, journal = {arXiv preprint arXiv:2603.18315}, year = {2026}, priority = {0}, }

2025

Featured

Sky-Drive: A Distributed Multi-Agent Simulation Platform for Socially-Aware and Human-AI Collaborative Future Transportation

Zilin Huang^*, Zihao Sheng^*, Zhengyang Wan^*, and

Sky-Drive is a distributed multi-agent simulation platform for socially aware driving and human-AI collaboration. It combines synchronized multi-terminal simulation, multimodal human-in-the-loop data collection, adaptive human-AI knowledge exchange, and digital twins of real transportation environments.

Journal of Intelligent and Connected Vehicles · 2025

Paper

Scholar 21 GitHub 9 ★

@article{huang2025sky,
  title = {Sky-Drive: A Distributed Multi-Agent Simulation Platform for Socially-Aware and Human-AI Collaborative Future Transportation},
  author = {Huang, Zilin and Sheng, Zihao and Wan, Zhengyang and Qu, Yansong and Luo, Yuhao and Wang, Boyue and Li, Pei and Chen, Yen-Jung and Chen, Jiancong and Long, Keke and Meng, Jiayi and Leng, Yue and Chen✉, Sikai},
  journal = {Journal of Intelligent and Connected Vehicles},
  year = {2025},
  priority = {1},
  url = {https://www.sciopen.com/article/10.26599/JICV.2026.9210070},
}

2024

J. Automob. Eng.

Featured

Study of the Tire Wear of Virtual Track Train

Hechao Zhou, Zhengyang Wan, Mingsu Mei, and

This study develops and validates a multibody dynamics model of a Virtual Track Train together with a finite-element tire model to calculate tire wear. The results identify tire pressure, vehicle load, pavement conditions, inclination angle, and tire slippage as critical influences on wear.

Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering · 2024

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@article{hechao2024tirewear,
  priority = {2},
  title = {Study of the Tire Wear of Virtual Track Train},
  author = {Zhou, Hechao and Wan, Zhengyang and Mei, Mingsu and Zhang✉, Jimin and Hecht, Markus},
  journal = {Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering},
  volume = {239},
  issue = {10-11},
  pages = {4608--4621},
  year = {2024},
  publisher = {Sage},
  url = {https://journals.sagepub.com/doi/abs/10.1177/09544070241280744},
}

CSCD
Development of Driving Simulation Platform for Virtual Track Train

Zhengyang Wan, Hechao Zhou, and Jimin Zhang✉

This work develops a Virtual Track Train driving simulator that combines a SIMPACK dynamics model, model-predictive control with Logitech driving hardware, and an Unreal Engine and Blender visualization interface. Simulations show that the platform captures operating performance across different driving modes.

Urban Rapid Rail Transit · 2024

Paper
@article{zhengyang2024vtt, priority = {2}, title = {Development of Driving Simulation Platform for Virtual Track Train}, author = {Wan, Zhengyang and Zhou, Hechao and Zhang✉, Jimin}, journal = {Urban Rapid Rail Transit}, volume = {0}, pages = {549--560}, year = {2024}, dimensions = {true} }