Industry Roundtable: Stefano Di Cairano, MERL – March 19, 2020
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Stefano Di Cairano
Distinguished Research Scientist, Senior Team Leader
Mitsubishi Electric Research Labs
Modular Design of Autonomous Vehicle Guidance and Control Architectures with Safety and Robustness Guarantees
Highly autonomous systems, such as autonomous vehicles are expected to exhibit complex behaviors in a changing and often unpredictable environment. As such they require an equally complex reasoning system to be encoded in a guidance, navigation, and control (GNC) architecture. Modularity of such an architecture allows to decompose the autonomous driving problem into computationally manageable subproblems, that can be addressed by exploiting the synergies of algorithms from different domain, e.g., robotic planning, statistical estimation, and control theory. While the integration of the different modules may be a daunting task, hard, time consuming and expensive, recent advances in control theory can be exploited to eliminate, or at least reduce, such difficulties, by enabling formal methods for integrating reasoning, planning and control modules. This talk explores the concepts of modular design, integration, and algorithm synergy, that allow to obtain GNC architectures for autonomous vehicles that are flexible, expandable, and provably safe and robust. The concepts are demonstrated by experimental results in full size vehicles, and a scaled testbench for autonomous driving system development.
Stefano Di Cairano received the Master (Laurea), and the PhD in Information Engineering in ’04 and ’08, respectively, from the University of Siena, Italy. He has been visiting student at the Technical University of Denmark and at the California Institute of Technology. During 2008–2011, he was with Powertrain Control R&A, Ford Research and Adv. Engineering, Dearborn, MI. Since 2011, he is with Mitsubishi Electric Research Laboratories, Cambridge, MA, where he is now the Senior Team Leader for Optimization-based Control, and a Distinguished Researcher in Control and Dynamical Systems. His research is on optimization-based control strategies for complex mechatronic systems, in automotive, factory automation, transportation systems and aerospace. His research interests include model predictive control, constrained control, particle filtering, hybrid systems, optimization. Dr. Di Cairano has authored/co-authored more than 150 peer reviewed papers in journals and conference proceedings and 35 patents. He was the Chair of the IEEE CSS Technical Committee on Automotive Controls, Chair of IEEE CSS Standing Committee on Standards and an Associate Editor of the IEEE Transactions on Control Systems Technology. He is currently the Vice-Chair of the IFAC Technical Committee on Optimal Control and the Chair of the Technology Conferences Editorial Board.
Faculty Host: Christos Cassandras