Ziran Wang

Assistant Professor

Purdue University, College of Engineering

ziran at purdue dot edu
HAMP G133, 550 Stadium Mall Drive, West Lafayette, Indiana 47907

Purdue DT Lab Website Updated CV

Announcements:
-Purdue DT Lab always looks for self-motivated talents to join as PhD/MS students or onsite/remote interns, and please refer to this page for more information.
-If you have research interests in using game engines (Unity and Unreal/CARLA) for autonomous driving simulation, please directly contact Dr. Ziran Wang.

Bio:

Dr. Ziran Wang is currently a Tenure-Track Assistant Professor at the Purdue University College of Engineering, part of the Autonomous and Connected Systems Initiative and the Lyles School of Civil Engineering. He leads the Purdue Digital Twin Lab (we are hiring), which aims to build digital replicas of real-world entities based on AI, big data, cloud/edge computing, and mixed reality.

Dr. Wang was a Principal Researcher at Toyota Motor North America R&D - InfoTech Labs, where he led the Digital Twin Roadmap. He received the Ph.D. degree from the University of California, Riverside under the supervision of Dr. Matthew J. Barth. His Ph.D. dissertation is "Developing Agent-Based Distributed Cooperative Vehicle-Infrastructure Systems in the Connected and Automated Vehicle Environment". Dr. Wang is an author of 60+ refereed papers and 50+ patent applications. His research achievements have been demonstrated at the Consumer Electronics Show (CES), and acknowledged by the National Center for Sustainable Transportation Dissertation Award issued by the U.S. Department of Transportation, and by the SAE Vincent Bendix Automotive Electronics Engineering Award (i.e., best paper in 2019) and the SAE Arch T. Colwell Merit Award (i.e., best paper in 2021).

Dr. Wang is serving various roles in multiple academic organizations, including:
-Associate editor of IEEE Internet of Things Journal, IEEE Transactions on Intelligent Vehicles, SAE International Journal of Connected and Automated Vehicles, and Frontiers in Sustainable Cities-Urban Transportation Systems and Mobility
-Guest editor of IEEE Open Journal of Intelligent Transportation Systems
-Handling editor of SAE Non-Event Technical Papers
-Founding committee chair in the IEEE Intelligent Transportation Systems Society, Technical Committee on Internet of Things in Intelligent Transportation Systems
-Program chair in the 4th IEEE Forum for Innovative Sustainable Transportation Systems
-Poster/demo co-chair in the 2023 IEEE Vehicular Networking Conference (VNC)
-Committee member in the IEEE Intelligent Transportation Systems Society, Technical Committee on Cooperative and Connected Vehicles, the IEEE Control Systems Society, Technical Committee on Smart Cities, and the IEEE Industrial Electronics Society, Technical Committee on Industrial Cyber-Physical Systems
-Technical program committee member in the 14th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), the 1st/2nd IEEE International Conference on Mobility: Operations, Services, and Technologies (MOST), the 2021 IEEE 94th Vehicular Technology Conference: VTC2021-Fall, and the 2021 IEEE International Conference on Digital Twin and Parallel Intelligence
-International advisory committee member in the 16th IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI 2022)

Check out the first-of-its-kind field test of large language model-enabled autonomous driving! More details can be found on the website.

An invited talk in Oct. 2022 that summarized Dr. Wang's research activities since 2017. More details can be found in the paper.

An invited talk and a panel discussion in Apr. 2023 that summarized Dr. Wang's research activities about digital twin simulation. More details can be found in the paper.

Highlights

Digital Twin

The Digital Twin is a digital replica of the physical entity in the real world, which leverages big data and cloud/edge computing to benefit the mobility system consisting of human, vehicle, and traffic.

Foundation Models for Autonomous Driving

Large language and vision models (LLVMs) can help autonomous vehicles understand the underlying logics of human commands and traffic scenes to facilitate human-autonomy teaming.

Personalized Behavior Modeling

Personalized driver behavior (or more generally human factor) plays a crucial role in human-autonomy teaming, especially when the driver is operating Advanced Driver Assistance Systems (ADAS).

Human-In-The-Loop Simulation

Driving simulator allows human to drive vehicles modeled in a virtual environment, enabling comparison between proposed connected and automated vehicle technology with human driving behaviors.

Cooperative Ramp Merging

With advanced Vehicle-to-Everything (V2X) communication techniques, connected and automated vehicles can schedule their sequence and plan their speed trajectories ahead of time to avoid last-second speed change at the merging point.

Cooperative Eco-Driving

With advanced Vehicle-to-Infrastructure (V2I) communication technique, "cooperative eco-driving" provides real-time advices to drivers based on real-time traffic and infrastructure conditions.