Integrating Autonomy into Traffic Systems: Scalable Control and Optimization

11:00am - 12:00pm
Zoom ID: 989 2634 7046; Passcode: INTR6800


Traffic systems are undergoing a fundamental transformation. The emergence of connected and autonomous vehicles (CAVs) provides tremendous opportunities for sustainability, efficiency, and safety. Before 100% CAVs are introduced in traffic systems, there will be a long transition period of mixed mobility, where both human-driven vehicles (HDVs) and CAVs co-exist. Timely and important questions are: can a small number of CAVs benefit urban mobility, and, if so, how? The design and operation of CAVs in traffic systems are becoming increasingly complex, due to system size, connectivity, and heterogeneity. This talk presents contributions in distributed control and scalable optimization techniques, which are critical for integrating autonomous vehicles into the existing traffic systems. In particular, we will provide theoretical evidence of the high potential of CAVs in mixed mobility, and further show how to exploit sparsity structures to design scalable and distributed algorithms that allow operating CAVs in large-scale mixed traffic systems. We will also introduce a new notion of Leading Cruise Control (LCC), in which CAVs not only maintain car-following operations but also lead the motion of HDVs. Specifically, LCC aims to both attenuate downstream traffic perturbations and smooth upstream traffic flow actively. This talk is based on our recent work:, and  

Event Format
Speakers / Performers:
Prof. Yang ZHENG
University of California, San Diego


Yang Zheng is an assistant professor in the ECE department at UC San Diego. Yang Zheng received the DPhil (Ph.D.) degree in Engineering Science from the University of Oxford in 2019. He received the B.E. and M.S. degrees from Tsinghua University in 2013 and 2015, respectively. From February 2019 to August 2020, he was a postdoctoral researcher in the SEAS and CGBC at Harvard University. He was a research associate at Imperial College London in 2021.


Dr. Zheng’s research interests include learning, optimization, and control of network systems, and their applications to cyber-physical systems, autonomous vehicles, and traffic systems. His work has been acknowledged by several awards, including the 2019 European PhD Award on Control for Complex and Heterogeneous Systems, the Best Student Paper Award Finalist at the 2019 European Control Conference, the Best Student Paper Award at the 17th IEEE International Conference on Intelligent Transportation Systems, and the Best Paper Award at the 14th Intelligent Transportation Systems Asia-Pacific Forum. He also received the National Scholarship, Outstanding Graduate in Tsinghua University, the Clarendon Scholarship at the University of Oxford, and the Chinese Government Award for Outstanding Self-financed Students Abroad.

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Prof. Huan YU

INTR Thrust, Systems Hub

Systems Hub, HKUST(GZ)
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