Jiawei Wang

I am currently a Lecturer in the Department of Civil and Environmental Engineering at the University of Michigan, Ann Arbor, and a Postdoctoral Research Fellow in the Mobility Transformation Lab, working with Prof. Henry X. Liu.

I received my Ph.D. degree in Mechanical Engineering at Tsinghua University in 2023, advised by Prof. Keqiang Li, and my Bachelor's Degree in Automotive Engineering at Tsinghua University in 2018. From Dec 2022 to Dec 2023, I was a visiting PhD student in the Automatic Control Laboratory at EPFL (École Polytechnique Fédérale de Lausanne), advised by Prof. Colin Jones. During my doctoral research, I also received guidance from Prof. Yang Zheng at UC San Diego.

Email / Google Scholar / Github

News

09/2025： Check our new preprint on TeraSim-World. Codes and videos are available here.
08/2025： Excited to begin my new role as a Lecturer in the CEE Department at University of Michigan!
07/2025： Our paper about risk-adjustable driving environment by conditional diffusion was accepted by ITSC 2025.
05/2025： Check our new preprint on generative behavior simulation for Autonomous Vehicles: TeraSim.
12/2024： I was awarded Beijing 2024 Outstanding Doctoral Dissertation Award!
11/2024： Our paper was accepted by IEEE T-ITS. Congratulations to my great collaborator Xu Shang!
11/2024： Our paper was accepted by TR Part C. Congratulations to my great collaborator Shuai Li!
08/2024： Check this demo for Green Wave Speed Advisory system in Mcity as part of the Smart Intersection Project.
06/2024： We're excited to invite you to participate in the Mcity AV Challenge!
02/2024： Check our new preprints on robust data-driven predictive control: Paper 1 and Paper 2.
01/2024： Our paper was accepted to ACC 2024.
10/2023： I was awarded the Distinguished Doctoral Dissertation Award from China SAE.
09/2023： Excited to start my new position as Postdoctoral Research Fellow in the Michigan Traffic Lab!

Research

My research interests lie at the intersection of learning, optimization, control, and simulation for urban mobility systems, with a particular emphasis on Connected and Automated Vehicles (CAVs). Precisely, I aim to advance CAVs' safety through high-fidelity simulation and advanced testing, while also pursuing reliable and scalable data-driven control strategies that promote broader societal and environmental benefits.

Selected Publications

Generative Simulation for Autonomous Driving

TeraSim-World: Worldwide Safety-Critical Data Synthesis for End-to-End Autonomous Driving
Jiawei Wang†, Haowei Sun†, Xintao Yan, Shuo Feng, Jun Gao, Henry X. Liu
Preprint, 2025
project page / arXiv

An automated pipeline for synthesizing safety-critical data for safe and scalable deployment of end-to-end autonomous driving at anywhere in the world.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion
Jiawei Wang, Xintao Yan, Yao Mu, Haowei Sun, Zhong Cao, Henry X. Liu
IEEE ITSC, 2025
arXiv

A simulation framework that generates statistically realistic and risk-adjustable traffic scenes using multi-agent conditional diffusion for stress testing of autonomous vehicle safety.

TeraSim: Uncovering Unknown Unsafe Events for Autonomous Vehicles through Generative Simulation
Haowei Sun†, Xintao Yan†, Zhijie Qiao†, Haojie Zhu†, Yihao Sun, Jiawei Wang, Shengyin Shen, Darian Hogue, Rajanikant Ananta, Derek Johnson, Greg Stevens, et al.
Preprint, 2025
arXiv / code

An open-source, high-fidelity traffic simulation platform designed to uncover unknown unsafe events and efficiently estimate AV statistical performance metrics.

Data-Driven Control and Digital-Twin Validation

	Decentralized Robust Data-Driven Predictive Control for Smoothing Mixed Traffic Flow Xu Shang, Jiawei Wang, Yang Zheng IEEE Transactions on Intelligent Transportation Systems, 2025 arXiv A decentralized robust data-driven predictive control framework for CAVs to smooth mixed traffic flow while ensuring computational scalability.
	Robust Data-Driven Predictive Control for Mixed Platoons under Noise and Attacks Shuai Li, Chaoyi Chen, Haotian Zheng, Jiawei Wang, Qing Xu, Jianqiang Wang, Keqiang Li Preprint, 2024 arXiv / related publication 1 / related publication 2 Robust data-driven predictive control framework for mixed platoons using reachability analysis to handle noise and attacks while ensuring safety.
	Implementation and Experimental Validation of Data-Driven Predictive Control for Dissipating Stop-and-Go Waves in Mixed Traffic Jiawei Wang, Yang Zheng, Jianghong Dong, Chaoyi Chen, Mengchi Cai, Keqiang Li, Qing Xu IEEE Internet of Things Journal, 2024 arXiv / video First experimental validation of data-driven predictive control for CAVs in dissipating traffic waves using miniature experiment platform.
	Distributed Data-Driven Predictive Control for Cooperatively Smoothing Mixed Traffic Flow Jiawei Wang, Yingzhao Lian, Yuning Jiang, Qing Xu, Keqiang Li, Colin N. Jones Transportation Research Part C: Emerging Technologies, 2023 arXiv / code A cooperative distributed data-driven predictive control framework for CAVs in large-scale mixed traffic flow.
	DeeP-LCC: Data-Enabled Predictive Leading Cruise Control in Mixed Traffic Flow Jiawei Wang, Yang Zheng, Keqiang Li, Qing Xu IEEE Transactions on Control Systems Technology, 2023 project page / arXiv / code / video A data-driven nonparametric strategy for safe and optimal control of CAVs in mixed traffic using Willems' fundamental lemma and receding horizon optimization.
	Mixed Cloud Control Testbed: Validating Vehicle-Road-Cloud Integration via Mixed Digital Twin Jianghong Dong, Qing Xu, Jiawei Wang, Chunying Yang, Mengchi Cai, Chaoyi Chen, Yu Liu, Jianqiang Wang, Keqiang Li IEEE Transactions on Intelligent Vehicles*, 2023 project page / arXiv A miniature experimental platform MCCT based on Mixed Digital Twin concept for validating multi-vehicle cooperation and vehicle-road-cloud integration.

Principled Understanding of Mixed Traffic

	Influence of Information Flow Topology and Maximum Platoon Size on Mixed Traffic Stability Shuai Li, Haotian Zheng, Jiawei Wang, Chaoyi Chen, Qing Xu, Jianqiang Wang, Keqiang Li Transportation Research Part C: Emerging Technologies*, 2025 paper Investigation on how the information flow topology ("looking ahead" or "looking behind") and the maximum platoon size influence the stability of mixed traffic flow.
	Leading Cruise Control in Mixed Traffic Flow: System Modeling, Controllability, and String Stability Jiawei Wang, Yang Zheng, Chaoyi Chen, Qing Xu, Keqiang Li IEEE Transactions on Intelligent Transportation Systems, 2022 project page / arXiv / code A novel Leading Cruise Control (LCC) framework for CAVs to actively lead the motion of the vehicles behind, while maintaining the car-following operations to the vehicles ahead.
	Cooperative Formation of Autonomous Vehicles in Mixed Traffic Flow: Beyond Platooning Keqiang Li†, Jiawei Wang†, Yang Zheng IEEE Transactions on Intelligent Transportation Systems, 2022 project page / arXiv / code Investigation of CAV formation patterns in mixed traffic from set-function optimization perspective, revealing optimal formations beyond platooning for system-level traffic benefits.
	Controllability Analysis and Optimal Control of Mixed Traffic Flow with Human-Driven and Autonomous Vehicles Jiawei Wang, Yang Zheng, Qing Xu, Jianqiang Wang, Keqiang Li IEEE Transactions on Intelligent Transportation Systems, 2021 project page / arXiv / code First rigorous proof of controllability and stabilizability properties of mixed traffic systems via CAVs with heterogeneous human-driven vehicles.
	Mixed Platoon Control of Automated and Human-Driven Vehicles at a Signalized Intersection: Dynamical Analysis and Optimal Control Chaoyi Chen, Jiawei Wang, Qing Xu, Jianqiang Wang, Keqiang Li Transportation Research Part C: Emerging Technologies, 2021 arXiv Proposes "1+n" control framework for CAV control at signalized intersections in mixed traffic, enabling the CAVs to significantly improve the traffic energy efficiency at a low penetration rate.
	Smoothing Traffic Flow via Control of Autonomous Vehicles Yang Zheng, Jiawei Wang, Keqiang Li IEEE Internet of Things Journal, 2020 project page / arXiv / code First rigorous theoretical analysis of controllability, stabilizability, and reachability of mixed traffic systems, showing that CAVs can effectively improve traffic with only 5% penetration rate.

Teaching

Instructor: CEE 551: Traffic Science, 2025.
Teaching Assistant: Vehicle Control Engineering, 2020; Calculus, 2020.

Service

Reviewer for Journal: IEEE Transactions on Intelligent Transportation Systems; Transportation Research Part C Emerging Technologies; IEEE Internet of Things Journal; IEEE Transactions on Transportation Electrification; IEEE Transactions on Intelligent Vehicles; IEEE Transactions on Control Systems Technology; IEEE Transactions on Control of Network Systems; IEEE Transactions on Vehicular Technology; IEEE Transactions on Consumer Electronics; Scientific Reports; IEEE/CAA Journal of Automatica Sinica; Transportation Science; Accident Analysis and Prevention; IET Intelligent Transport Systems; Automotive Innovation; Optimal Control, Applications and Methods; International Journal of Systems Science; Asian Journal of Control; ACM Transactions on Cyber-Physical Systems; Journal of the Franklin Institute; Chinese Journal of Mechanical Engineering; Sensors; World Electric Vehicle Journal.
Reviewer for Conference: IEEE Conference on Decision and Control (CDC); IFAC World Congress (IFAC); American Control Conference (ACC); IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); International Symposium on Transportation and Traffic Theory (ISTTT); TRB Annual Meeting; Learning for Dynamics and Control (L4DC); IEEE International Conference on Intelligent Transportation (ITSC); IEEE Intelligent Vehicles Symposium (IV); ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS); COTA International Conference for Transportation Professionals (CICTP); ITS World Congress (ITSW); Modeling, Estimation and Control Conference (MECC).
Editorial Assistant: Journal of Intelligent Transportation Systems.
Organizer: Mcity AV Challenge.
Demonstrator: Autonomous Driving Demonstration at Tsinghua University, 2018-2020.
Volunteer: 14th International Symposium on Advanced Vehicle Control (AVEC).

Selected Awards & Scholarships

2024, Beijing Outstanding Doctoral Dissertation Award
2023, Distinguished Doctoral Dissertation Award, China Society of Automotive Engineers
2023, Outstanding Ph.D. Graduate, Tsinghua University
2023, Excellent Doctoral Dissertation Award, Tsinghua University
2022, National Scholarship, Tsinghua University
2020, National Scholarship, Tsinghua University
2018, Best Paper Award in the 18th COTA International Conference for Transportation Professionals (CICTP)
2016, Outstanding Student Leader Award, Tsinghua University
2015, Outstanding Volunteer Scholarship, Tsinghua University
2015, National Scholarship, Tsinghua University (Top 1 undergraduate in year 1)

Based on the template from this site.