Research Review with Hadi Meidani, Ph.D.
Speaker(s): Hadi Meidani, Ph.D., Assistant Professor of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign
Presentation Title: Using Artificial Intelligence for Optimal Truck Platooning Under Uncertainties
Research: Operations of Connected and Autonomous Freight Trucks under Congestion and Infrastructure Cost Considerations
Date/Time: Wednesday, March 31st, 2021 | 1:00 PM ET
Continuing Education Units (CEU): .1*
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Abstract: Truck platooning is the process of using connected vehicle technology to join two or more trucks in a convoy. Platooning is associated with two, major societal benefits: environmental, through lowered fuel consumption, CO2 emission, and traffic efficiency, and safety improvement, through automated driving. Quantification of fuel consumption in platoons depends on the computational fluid dynamics (CFD) of the system, specifically the resistance or drag force of trucks. While optimization of fuel consumption is pivotal in truck platooning, analysis of CFD is computationally expensive, especially when uncertainties are present, due to geometrical variability of trucks and platoons as well as in wind magnitude and direction.
This research proposes an artificial intelligence-based surrogate model which enables near real-time optimization of platoon configurations based on fuel consumption and impacts on pavement conditions. Attendees will learn how a deep neural network (DNN) model can be trained using data from CFD simulations that utilize high-performance computing (HPC) resources.
Dr. Meidani is an Assistant Professor of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign. He earned his Ph.D. in Civil Engineering and M.S. in Electrical Engineering from the University of Southern California in 2012. Prior to joining UIUC, he was a postdoctoral research associate in the Department of Aerospace and Mechanical Engineering at USC in 2012 and in the Scientific Computing and Imaging Institute at the University of Utah in 2013. He is the recipient of the NSF CAREER Award for studying fast computational models for energy-transportation systems. He is also the recipient of the student paper award in probabilistic methods at the ASCE Engineering Mechanics Institute Conference in 2012. His research interests are uncertainty quantification, scientific machine learning, and decision-making under uncertainty.