Machine Learning in Intelligent Transportation Systems from the Control and Robotics Prospective 

 

Prof. Dr. Žarko Ćojbašić
University of Niš
Faculty of Mechanical Engineering in Niš

 

Abstract: Intelligent transportation systems (ITS) have become an essential component of urban planning and smart cities, playing a crucial role in enhancing traffic safety, transportation efficiency, energy conservation, and environmental preservation. ITSs encompass a wide array of services and applications, including road traffic management, traveler information systems, public transit system management, autonomous vehicles, and more. However, ITSs also produce various challenges such as scalability, diverse quality-of-service requirements, and the handling of vast data volumes generated. Some important challenges, as well as many features of ITS, are closely tied to control and robotics tasks and applications..

Machine Learning in Intelligent Transportation Systems from the Control and Robotics Prospective 

 

Prof. Dr. Žarko Ćojbašić
University of Niš
Faculty of Mechanical Engineering in Niš

 

Abstract: Intelligent transportation systems (ITS) have become an essential component of urban planning and smart cities, playing a crucial role in enhancing traffic safety, transportation efficiency, energy conservation, and environmental preservation. ITSs encompass a wide array of services and applications, including road traffic management, traveler information systems, public transit system management, autonomous vehicles, and more. However, ITSs also produce various challenges such as scalability, diverse quality-of-service requirements, and the handling of vast data volumes generated. Some important challenges, as well as many features of ITS, are closely tied to control and robotics tasks and applications. This lecture explores the use of machine learning (ML) in enabling ITS, particularly from the perspective of control and robotics. The current state-of-the-art in ML technology application for various ITS-related control and robotics solutions is considered, such as autonomous cars and computer vision-related tasks. The primary objective has been to identify potential future directions for the implementation of ML enabled control and robotics in ITS and to ascertain how ML technology can further enhance their capabilities and efficiency.

Key words: Machine Learning, Artificial Intelligence, Intelligent Transportation Systems, Control Systems, Robotics

 

Biography: Žarko Ćojbašić is a full professor of the Mechanical Engineering Faculty, University of Niš, Serbia and is affiliated with its Department of Mechatronics and Control, acting as the Head of Control Systems Laboratory. He is a Senior Member of IEEE and currently acting as an elected Chair of the Computational Intelligence Chapter of IEEE Serbia and Montenegro Section. During the last two years he has been also affiliated with the Mechanical Engineering Faculty of the University of Belgrade, Serbia.

He was the leader and participant of numerous research and educational projects, and within frameworks of projects and research grants he stayed at many foreign universities such as Technical University of Berlin Germany, University of Vigo Spain, Imperial College London UK, University of Bremen Germany, University of Exeter UK, Polytechnic University of Catalonia Barcelona Spain, and others. His research interests include control systems and robotics, intelligent control systems, computational intelligence, mechatronics and biomedical engineering.

He has published around 80 papers in journals from ISI/SCI list, while his citation indexes are h=24 with approximately 2000 citations at SCOPUS and h=26 with approximately 2600 citations at Google Scholar. He was an invited lecturer, member of the scientific committees and participant of numerous conferences.

Professor Žarko Ćojbašić is currently acting as a contracted European Union expert in the fields of robotics, control, computational intelligence and biomedical engineering.


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