Keynote Speakers

At least five keynote speeches have been planned throughout the symposium. The details of the keynote speakers will be available in a short time.

Bart De Schutter

Bart De Schutter received the PhD degree in Applied Sciences in 1996, at K.U.Leuven, Belgium. After obtaining his PhD degree, he was a postdoctoral researcher at the SISTA-ESAT group of K.U.Leuven, Belgium. In 1998 he moved to the Control Lab of Delft University of Technology as an assistant professor. In 2000 he became associate professor. Currently, he is a full professor at the Delft Center for Systems and Control of Delft University of Technology in Delft, The Netherlands.
Bart De Schutter is associate editor of Automatica and of the IEEE Transactions on Intelligent Transportation Systems.  His current research interests include freeway and urban traffic control, control of large-scale transportation networks, intelligent vehicles, control of hybrid systems, multi-agent systems, and optimization.

Presentation Title: Multi-level control of large-scale traffic networks
Carlo van de Weijer

After getting his Master degree in Mechanical Engineer at the University of Technology in Eindhoven, Carlo van de Weijer (1966) started in 1990 at TNO Automotive in Delft, where he amongst others was responsible for setting up a new research field in Electrical and Hybrid drive trains and, from 1998, was responsible for the Business Unit Power Trains. In 1997 he received his PhD with honors from Technical University in Graz, Austria.
In 2001 he moved to SiemensVDO in Eindhoven to head the local R&D lab for automotive navigation systems. In 2007 he joined TomTom to become Vice President Traffic Solutions. Currently, Dr. Van de Weijer is director of the Strategic Area Smart Mobility at Eindhoven University of Technology, alongside his work for TomTom. He is member of the board of amongst others ITS Netherlands, AutomotiveNL and the EU ITS Action Plan Advisory Board and is member of the supervisory board of several hightech companies.

Presentation Title: Disrupted mobility
Petros A. Ioannou

Petros A. Ioannou received the B.Sc. degree with First Class Honors from University College, London, England, in 1978 and the M.S. and Ph.D. degrees from the University of Illinois, Urbana, Illinois, in 1980 and 1982, respectively. In 1982, Dr. Ioannou joined the Department of Electrical Engineering-Systems, University of Southern California, Los Angeles, California. He is currently a Professor in the same Department and the Director of the Center of Advanced Transportation Technologies and Associate Director for Research of METRANS, a University Transportation Center. He also holds a courtesy appointment with the Department of Aerospace and Mechanical Engineering and the Department of Industrial Engineering. His research interests are in the areas of adaptive control, neural networks, nonlinear systems, vehicle dynamics and control, intelligent transportation systems and marine transportation. Dr. Ioannou was the recipient of the Outstanding Transactions Paper Award by the IEEE Control System Society in 1984 and the recipient of a 1985 Presidential Young Investigator Award for his research in Adaptive Control. In 2009 he received the IEEE ITSS Outstanding ITS Application Award and the IET Heaviside Medal for Achievement in Control by the Institution of Engineering and Technology (former IEE). In 2012 he received the IEEE ITSS Outstanding ITS Research Award and in 2015 the 2016 IEEE Transportation Technologies Award. Dr. Ioannou is a Fellow of IEEE, Fellow of International Federation of Automatic Control (IFAC), Fellow of the Institution of Engineering and Technology (IET), and the author/co-author of 8 books and over 300 research papers in the area of controls, vehicle dynamics, neural networks, nonlinear dynamical systems and intelligent transportation systems.

Presentation Title: Intelligent Personalized Driving Assist in Urban Environments

Abstract. Driving in an urban environment can be frustrating due mainly to lack of adequate information regarding traffic conditions, parking, navigation etc. Since drivers have different driving characteristics, reaction times and preferences an effective driver assist system should be personalized.
In this talk we present an approach of learning the individual driver characteristics and habits and used what is learned to provide personalized information to driver regarding estimated travel times, traffic predictions, parking assist etc. The tasks of vehicle following, lane changing, merging, stopping at intersections are modeled for each individual driver and the driver model is used to provide personalized assistance. We present a parking assist system, which is integrated with personalized driving in order to provide accurate predictions of traffic at the time of expected arrival based on what the driver prefers.