CISE Seminar: October 9, 2020 – Andreas Malikopoulos, CISE Resident Scholar

Zoom Link: https://bostonu.zoom.us/j/9465617524
Meeting ID: 946 561 7524
3:00pm – 4:00pm

Andreas A. Malikopoulos, University of Delaware
CISE Resident Scholar 

 

 

Optimal Path Planning and Coordination for Connected and Automated Vehicles

Connected and automated vehicles (CAVs) provide the most intriguing opportunity for enabling users to better monitor transportation network conditions and make better operating decisions to improve safety and reduce pollution, energy consumption, and travel delays. CAVs are typical cyber-physical systems where the cyber component (e.g., data and shared information through vehicle-to-vehicle and vehicle-to-infrastructure communication) can aim at optimally controlling the physical entities (e.g., CAVs, non-CAVs). The cyber-physical nature of such systems is associated with significant control challenges and gives rise to a new level of complexity in modeling and control. As we move to increasingly complex emerging mobility systems, new control approaches are needed to optimize the impact on system behavior of the interplay between vehicles at different traffic scenarios. In this talk, I will present a decentralized control framework for coordination of CAVs in different traffic scenarios, e.g., merging at roadways and roundabouts, crossing unsignalized intersections, cruising in congested traffic, passing through speed reduction zones, and lane-merging or passing maneuvers. The framework includes: (1) an upper-level optimization that yields for each CAV its optimal path, including the time and lane, to pass through a given traffic scenario by alleviating congestion; and (2) a low-level optimization that yields for each CAV its optimal control input (acceleration/deceleration) to achieve the optimal path and time derived in the upper-level. I will provide a geometric duality framework using hyperplanes to prove strong duality of the upper-level optimization problem. The latter implies that the optimal path and time for each CAV does not activate any of the state, control, and safety constraints of the low-level optimization, thus allowing for online implementation.

Andreas Malikopoulos is the Terri Connor Kelly and John Kelly Career Development Professor in the Department of Mechanical Engineering at the University of Delaware (UD). Before joining UD, he was the Deputy Director and the Lead of the Sustainable Mobility Theme of the Urban Dynamics Institute at Oak Ridge National Laboratory, and a Senior Researcher with General Motors Global Research & Development. He received a Diploma from the National Technical University of Athens, Greece, in 2000, and his M.S. and Ph.D. degrees from the University of Michigan, Ann Arbor, in 2004 and 2008, respectively all in Mechanical Engineering. His research interests span several fields, including analysis, optimization, and control of cyber-physical systems; decentralized stochastic systems; stochastic scheduling and resource allocation; and learning in cyber-physical systems. Dr. Malikopoulos is the recipient of several prizes and awards, including the 2007 Dare to Dream Opportunity Grant from the University of Michigan Ross School of Business, the 2007 University of Michigan Teaching Fellow, the 2010 Alvin M. Weinberg Fellowship, the 2019 IEEE Young Researcher Award, and the 2020 College of Engineering Outstanding Junior Faculty Award. He has been selected by the National Academy of Engineering to participate at the 2010 German-American Frontiers of Engineering (FOE) Symposium and organize a session in transportation at the 2016 European-American FOE Symposium. He has also been selected as a 2012 Kavli Frontiers of Science Scholar by the National Academy of Sciences. Dr. Malikopoulos is currently an Associate Editor of the IEEE Transactions on Intelligent Vehicles, IEEE Transactions on Intelligent Transportation Systems, and Automatica. He is a Senior Member of the IEEE and a Fellow of the ASME.

Faculty Host: Christos Cassandras
Student Host: Erfan Aasi