Jiawei Wang

Postdoctoral Research Fellow

University of Michigan

Biography

I am currently a Postdoctoral Research Fellow in the Michigan Traffic Lab of the University of Michigan, Ann Arbor, 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. During my PhD study, I was also advised by Prof. Yang Zheng from University of California, San Diego. 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.

I received the Outstanding Ph.D. Graduate, the Excellent Doctoral Dissertation Award, and the National Scholarship from Tsinghua University. I was the recipient of the Distinguished Doctoral Dissertation Award from China Society of Automotive Engineers (China SAE) in 2023, the Annual Best Paper Award for the Journal of Transport Information and Safety in 2021, and the Best Paper Award at the 18th COTA International Conference of Transportation Professionals in 2018.

Interests

Connected Automated Vehicles
Intelligent Transportation
Control and Optimization
Machine Learning

Education

PhD in Mechanical Engineering, 2023
Tsinghua University
Visiting PhD in Automatic Control Lab, 2022
EPFL (École Polytechnique Fédérale de Lausanne)
BSc in Automotive Engineering, 2018
Tsinghua University

News

03/2025: I was award Beijing 2024 Outstanding Doctoral Dissertation Award!
11/2024: Our paper Decentralized Robust Data-driven Predictive Control for Smoothing Mixed Traffic Flow was accepted for publication in IEEE Transactions on Intelligent Transportation Systems. Congratulations to my great collaborator Xu Shang!
11/2024: Our paper Influence of information flow topology and maximum platoon size on mixed traffic stability was accepted for publication in Transportation Research Part C. Congratulations to my great collaborator Shuai Li!
08/2024: Check this video for our demonstration of Green Wave Speed Advisory system in Mcity as part of the Smart Intersection Project. Thanks to the great support from Mcity and TOYOTA!
06/2024: We’re excited to invite you to participate in the Mcity AV Challenge! I am honored to be one of the organizers of this prestigious event and look forward to your involvement.
02/2024: Check our new preprints on robust data-driven predictive control for CAVs in mixed traffic: Decentralized Robust Data-driven Predictive Control for Smoothing Mixed Traffic Flow and Robust Data-EnablEd Predictive Leading Cruise Control via Reachability Analysis.
01/2024: Our paper Smoothing mixed traffic with robust data-driven predictive control for connected and autonomous vehicles was accepted to 2024 American Control Conference.
10/2023: I was awarded the Distinguished Doctoral Dissertation Award from China Society of Automotive Engineers (China SAE).
09/2023: I started my new position as Postdoctoral Research Fellow in the Michigan Traffic Lab of the University of Michigan, Ann Arbor, working with Prof. Henry X. Liu.
07/2023: Our paper Distributed data-driven predictive control for cooperatively smoothing mixed traffic flow was accepted for publication in Transportation Research Part C.
07/2023: Our paper Implementation and Experimental Validation of Data-Driven Predictive Control for Dissipating Stop-and-Go Waves in Mixed Traffic was accepted for publication in the IEEE Internet of Things Journal.
06/2023: Our paper Cloud-based Connected Vehicle Control under Time-Varying Delay: Stability Analysis and Controller Synthesis was accepted for publication in the IEEE Transactions on Vehicular Technology.
06/2023: Our paper DeeP-LCC: Data-EnablEd Predictive Leading Cruise Control in Mixed Traffic Flow was accepted for publication in the IEEE Transactions on Control Systems Technology.
06/2023: I received the Outstanding Ph.D. Graduate and the Excellent Doctoral Dissertation Award from Tsinghua University.
05/2023: I passed the public defense for my PhD thesis!
02/2023: Our paper Mixed Cloud Control Testbed: Validating Vehicle-Road-Cloud Integration via Mixed Digital Twin was accepted for publication in the IEEE Transactions on Intelligent Vehicles.
01/2023: I finished my visit study at the Automatic Control Laboratory of EPFL. Great thanks to Prof. Colin Jones for hosting me and all the colleagues in the lab for helping me through the past year!
10/2022: Check out our new preprint: Distributed data-driven predictive control for cooperatively smoothing mixed traffic flow.
10/2022: I was awarded National Scholarship at Tsinghua University.
07/2022: Invited talk on Smoothing Traffic Flow via Connected and Autonomous Vehicles at the EPFL, hosted by Prof. Maryam Kamgarpour.
04/2022: Check out our new preprint: Implementation and Experimental Validation of Data-Driven Predictive Control for Dissipating Stop-and-Go Waves in Mixed Traffic, and the two videos on the experiments: Video 1 and Video 2.
01/2022: Our paper Data-Driven Predictive Control for Connected and Autonomous Vehicles in Mixed Traffic was accepted to 2022 American Control Conference.
01/2022: I started my visiting study at the Automatic Control Laboratory in EPFL (École Polytechnique Fédérale de Lausanne), advised by Prof. Colin Jones!

Featured Publications

For the full list of my publication, please see my Google Scholar page.

Jiawei Wang, Yingzhao Lian, Yuning Jiang, Qing Xu, Keqiang Li, Colin N. Jones

October, 2023 Transportation Research Part C: Emerging Technologies

Distributed data-driven predictive control for cooperatively smoothing mixed traffic flow

This paper proposes a cooperative DeeP-LCC (Data-EnablEd Predictive Leading Cruise Control) formulation and its distributed implementation algorithm. In cooperative DeeP-LCC, the traffic system is naturally partitioned into multiple subsystems with one single CAV, which collects local trajectory data for subsystem behavior predictions based on the Willems’ fundamental lemma. Meanwhile, the cross-subsystem interaction is formulated as a coupling constraint. Then, we employ the Alternating Direction Method of Multipliers (ADMM) to design the distributed DeeP-LCC algorithm. This algorithm achieves both computation and communication efficiency, as well as trajectory data privacy, through parallel calculation. Our simulations on different traffic scales verify the real-time wave-dampening potential of distributed DeeP-LCC, which can reduce fuel consumption by over 31.84% in a large-scale traffic system of 100 vehicles with only 5%–20% CAVs.

Jiawei Wang, Yang Zheng, Keqiang Li, Qing Xu

July, 2023 IEEE Transactions on Control Systems Technology

DeeP-LCC: Data-EnablEd Predictive Leading Cruise Control in Mixed Traffic Flow

In this article, instead of relying on a parametric car-following model, we introduce a data-driven nonparametric strategy, called Data-EnablEd Predictive Leading Cruise Control (DeeP-LCC), to achieve safe and optimal control of CAVs in mixed traffic. We first utilize Willems’ fundamental lemma to obtain a data-centric representation of mixed traffic behavior. This is justified by rigorous analysis on controllability and observability properties of mixed traffic. We then employ a receding horizon strategy to solve a finite-horizon optimal control problem at each time step, in which input–output constraints are incorporated for collision-free guarantees. Numerical experiments validate the performance of DeeP-LCC compared to a standard predictive controller that requires an accurate model. Multiple nonlinear traffic simulations further confirm its great potential on improving traffic efficiency, driving safety, and fuel economy.

Jianghong Dong, Qing Xu, Jiawei Wang, Chunying Yang, Mengchi Cai, et al

April, 2023 IEEE Transactions on Intelligent Vehicles

Mixed cloud control testbed: Validating vehicle-road-cloud integration via mixed digital twin

In this paper, we introduce our miniature experimental platform, Mixed Cloud Control Testbed (MCCT), developed based on a new notion of Mixed Digital Twin (mixedDT). Combining Mixed Reality with Digital Twin, mixedDT integrates the virtual and physical spaces into a mixed one, where physical entities coexist and interact with virtual entities via their digital counterparts. Under the framework of mixedDT, MCCT contains three major experimental platforms in the physical, virtual and mixed spaces respectively, and provides a unified access for various human-machine interfaces and external devices such as driving simulators. A cloud unit, where the mixed experimental platform is deployed, is responsible for fusing multi-platform information and assigning control instructions, contributing to synchronous operation and real-time cross-platform interaction. Particularly, MCCT allows for multi-vehicle coordination composed of different multi-source vehicles (e.g., physical vehicles, virtual vehicles and human-driven vehicles). Validations on vehicle platooning demonstrate the flexibility and scalability of MCCT.

Jiawei Wang, Yang Zheng, Chaoyi Chen, Qing Xu, Keqiang Li

August, 2022 IEEE Transactions on Intelligent Transportation Systems

Leading cruise control in mixed traffic flow: System modeling, controllability, and string stability

In this paper, we introduce a notion of Leading Cruise Control (LCC), in which the CAV maintains car-following operations adapting to the states of its preceding vehicles, and also aims to lead the motion of its following vehicles. Specifically, by controlling the CAV, LCC aims to attenuate downstream traffic perturbations and smooth upstream traffic flow actively. We first present the dynamical modeling of LCC, with a focus on three fundamental scenarios: car-following, free-driving, and Connected Cruise Control. Then, the analysis of controllability, observability, and head-to-tail string stability reveals the feasibility and potential of LCC in improving mixed traffic flow performance. Extensive numerical studies validate that the capability of CAVs in dissipating traffic perturbations is further strengthened when incorporating the information of the vehicles behind into the CAVs’ control.

Jiawei Wang, Yang Zheng, Qing Xu, Jianqiang Wang, Keqiang Li

December, 2021 IEEE Transactions on Intelligent Transportation Systems

Controllability Analysis and Optimal Control of Mixed Traffic Flow With Human-Driven and Autonomous Vehicles

This paper analyzes the controllability of mixed traffic systems and designs a system-level optimal control strategy. Using the Popov-Belevitch-Hautus (PBH) criterion, we prove for the first time that a ring-road mixed traffic system with one CAV and multiple heterogeneous human-driven vehicles is not completely controllable, but is stabilizable under a very mild condition. Then, we formulate the design of a system-level control strategy for the CAV as a structured optimal control problem, where the CAV’s communication ability is explicitly considered. Finally, we derive an upper bound for reachable traffic velocity via controlling the CAV. Extensive numerical experiments verify the effectiveness of our analytical results and the proposed control strategy. Our results validate the possibility of utilizing CAVs as mobile actuators to smooth traffic flow actively.

Awards and Scholarships

Awards

2025, 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
2023, Best Presentation Award, the First National Doctoral Forum by China Society of Automotive Engineers
2021, 1st Prize, Annual Best Paper Award in Journal of Transport Information and Safety
2018, Best Paper Award in the 18th COTA International Conference for Transportation Professionals
2018, Outstanding Undergraduate Thesis Award, Department of Automotive Engineering at Tsinghua University
2016, Outstanding Student Leader Award, Tsinghua University

Scholarships

2022, National Scholarship, Tsinghua University
2020, National Scholarship, Tsinghua University
2020, CSC Scholarship for Visiting PhD Study at EPFL
2019, Comprehensive Scholarship, Tsinghua University (Top 3 graduates in year 1)
2017, Comprehensive Scholarship, Tsinghua University (Top 3 undergraduates in year 3)
2016, Comprehensive Scholarship, Tsinghua University (Top 3 undergraduates in year 2)
2015, Outstanding Volunteer Scholarship, Tsinghua University
2015, National Scholarship, Tsinghua University (Top 1 undergraduate in year 1)

Service

Reviewer for Journal: IEEE Transactions on Intelligent Transportation Systems; Transportation Research Part C Emerging Technologies; IEEE Internet of Things Journal; IEEE Transactions on Intelligent Vehicles; IEEE Transactions on Control Systems Technology; IEEE Transactions on Control of Network Systems; IEEE Transactions on Vehicular Technology; 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.

Teaching Assistant: Vehicle Control Engineering, 2020; Calculus, 2020.

Demonstrator: Autonomous Driving Demonstration at Tsinghua University, 2018-2020.

Volunteer: 14th International Symposium on Advanced Vehicle Control (AVEC).

Contact

jiawe@umich.edu