报告人简介

Dr. Huan Yu is an Assistant Professor at the Hong Kong University of Science and Technology (Guangzhou), jointly appointed with the Intelligent Transportation Thrust and the Robotics and Autonomous Systems Thrust. She received the B.Sc. degree from Northwestern Polytechnical University, and the M.Sc. and Ph.D. degrees in mechanical and aerospace engineering from the University of California, San Diego, San Diego. She was a visiting scholar at University of California, Berkeley and Massachusetts Institute of Technology. Her research has been focused on finding safe, stable and robust solutions that combine control theory, machine learning, and traffic flow theory to advance the boundaries of intelligent transportation systems.

报告摘要

Emerging technologies—autonomous driving, wireless communication, and AI—are transforming urban mobility systems, from road traffic infrastructure to connected and autonomous vehicles (CAVs). Urban mobility systems encompass vehicles, traffic flow, and transportation networks, where macro-level traffic regulation interacts with micro-level vehicular behaviors to shape congestion, efficiency, and safety. In this talk, I will explore how control theory can bridge the macro-micro loop to optimize urban mobility. I will begin with infrastructure-based traffic control, where macroscopic sensing and actuation (e.g., variable speed limits, ramp metering) are designed using PDE-based control framework, for mitigating stop-and-go traffic waves—a critical instability in congested freeway. Building on this, I will discuss how CAVs enable fine-grained sensing and control at the microscopic level. Compared with human-driven vehicles (HVs), CAVs offer enhanced actuation and perception, unlocking new opportunities for traffic stabilization and safety. Specifically, I will introduce a safety-critical traffic control framework leveraging control barrier functions (CBFs), which provides collision-free guarantees for mixed autonomy traffic (CAVs and HVs). I will also highlight how physics-informed and neural operator machine learning can further enhance control designs, merging theoretical rigor with data-driven adaptability for next-generation intelligent transportation.