Presented By: Center for Connected and Automated Transportation
Lagranian Control at Large and Local Scales in Mixed Autonomy Traffic Flows
Professor Alexandre Bayen
The CCAT Distinguished Lecture Series returns this May with Professor Alexandre Bayen, the Liao-Cho Professor of Engineering at UC Berkeley! This talk investigates Lagrangian (mobile) control of traffic flow at local scale (vehicular level), and how self-driving vehicles will change traffic flow patterns. Professor Bayen describes approaches based on deep, reinforcement learning presented in the context of enabling mixed-autonomy mobility. This lecture also explores the gradual and complex integration of automated vehicles into the existing traffic system. Attendees will learn the potential impact of a small fraction of automated vehicles on low-level traffic flow dynamics, using novel techniques in model-free, deep reinforcement learning, in which the automated vehicles act as mobile (Lagrangian) controllers to traffic flow.
Illustrative examples will be presented in the context of a new, open-source computational platform called FLOW, which integrates state-of-the-art microsimulation tools with deep-RL libraries on AWS EC2. Interesting behavior of mixed autonomy traffic will be revealed in the context of emergent behavior of traffic: https://flow-project.github.io/
Illustrative examples will be presented in the context of a new, open-source computational platform called FLOW, which integrates state-of-the-art microsimulation tools with deep-RL libraries on AWS EC2. Interesting behavior of mixed autonomy traffic will be revealed in the context of emergent behavior of traffic: https://flow-project.github.io/
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