From Micro to Macroscopic: Leveraging Connected Automated Vehicles for Traffic Control

Dr. Yu, Huan

Assistant Professor, The Hong Kong University of Science and Technology (Guangzhou)
Affiliate Assistant Professor, The Hong Kong University of Science and Technology (HKUST)

Seminar Information

Seminar Series

Dynamic Systems & Controls

Seminar Date - Time

January 26, 2024, 3:00 pm

-

4 PM

Seminar Location

Remote

Important Links

Zoom Meeting ID: 977 1418 2371

Abstract

Emerging technologies of autonomous driving and wireless communications enable vehicles that used to be “physically-connected, human-controlled” become “cyber-connected, computer-programmed”. Leveraging enhanced control and sensing capabilities beyond those of human-driven vehicles (HV), connected automated vehicles (CAVs) are expected to substantially improve the efficiency, stability, and safety of transportation systems. In mixed-autonomy traffic that CAVs and HVs coexist, the primary challenges are two-fold: modeling the cruising and lane-changing dynamics of CAVs from microscopic and macroscopic level and designing CAV-based control strategies to optimize traffic through information sharing and vehicle coordination. This talk will first focus on CAV-based control problems in a microscopic mixed traffic. Cruising control strategies of CAVs are developed to not only guarantee smooth and safe driving behaviors of the ego vehicle, but also to stabilize the surrounding HVs. In particular, a safety-critical traffic control framework will be introduced, employing control barrier functions to impart collision-free safety guarantees for both the CAV and HVs. In the second part, macroscopic Partial Differential Equation models and backstepping control design will be introduced for mitigating stop-and-go traffic in freeway. The discussion will highlight how traffic state estimation can be improved using traffic field data and learning-based control approaches.

Speaker Bio

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, and an Affiliate Assistant Professor in the Department of Civil and Environmental Engineering at the Hong Kong University of Science and Technology (HKUST). 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, United States. She was a visiting scholar at the 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.