Many real-life problems, including Automated Driving, can be formulated as sequential decisionmaking and control tasks. Solving these tasks is a major topic of Control Theory, Artificial Intelligence and Robotics, with complementary methods roughly grouped as Control, Planning and Learning. Planning relies on deliberative reasoning about the current state and sequence of future reachable states to solve the problem. Learning, on the other hand, is focused on improving system performance based on experience or available data. Combining these methods by learning to improve the performance of planning, based on experience in similar, previously solved problems, is ongoing research.
This talk provides a concise introduction to basic Planning and Learning methods, specifics of Automated Driving problems and a state-of-the-art combined Planning and Learning approach.
About the speaker:
Zlatan Ajanovic is Senior researcher at VIRTUAL VEHICLE Research GmbH, Graz (Austria). He started his career in automotive in 2011 and held several positions since then, in Prevent Group (Bosnia
and Herzegovina) and AVL List (Austria). He received Bachelor and Master degree from the Univesity of Sarajevo and a Ph.D. degree from the Graz University of Technology, all focused on Automation and Control.
Through ITEAM project, as a Marie Curie Fellow, he was a visiting researcher at TU Delft, University of Sarajevo, AVL List and Volvo Cars.
Currently, he serves as a member of the IFAC Technical Committee for Intelligent Autonomous Vehicles. His current research interests include
Planning, Learning and Control methods applied to Autonomous Vehicles. He publishes regularly on major Robotics, Artificial Intelligence and Control events, and he is the recipient of the IFAC Young Author Award and Hans List Scholarship.