doc. Ing. Jiří Vokřínek, Ph.D.

The topic focusses on the domain of autonomous agents for intelligent vehicles. It also address information exchange of cooperating agents needed for advanced drive assistive systems with a high degree of autonomy. The researched techniques and algorithms will be validated on realistic simulations. The main stream of research should target assistive systems based on realistic level of in-car autonomous agents cooperation providing better performance and safety of the both drive and traffic. http://cs.felk.cvut.cz/en/people/xvokrine

The research topic focuses on the application of modern AI approaches to solving Dial and Ride mobility domain problems, particularly within combined public transport (PT) and Mobility on Demand (MoD) systems. Traditionally, research has focused on one aspect only (either PT or MoD), and classical solutions are not very scalable to real-world problem instances or complex optimization criteria. Combining these two hard problems introduces significant new research challenges. Emphasis should be placed on multi-criteria planning and optimization using machine learning techniques applicable to large-scale realistic instances. Additionally, the research should address demand forecasting, dynamic transport aspects, and passengers' behavioral considerations.