The goals of the research are to design vehicle control for achieving autonomous functionalities and to examine their implementation possibilities on small-scaled indoor vehicles and on real test vehicles.
The scientific background of the research is based on the robust and nonlinear control theory, which is used to design architectures with provable performance guarantees on the controlled system. In the design process learning-based methods are incorporated, which requires analysis on the stability and on the performance of the controlled system, especially on the safety-critic vehicle control applications. The effectiveness of the resulted methods are validated through real-life vehicle control scenarios.
The development and analysis of machine learning algorithms and optimization methods is a new direction in the planning of autonomous vehicle control. In addition, providing stability and performance guarantees for complex management architectures is a significant challenge. Research on validation and safety assurance for autonomous vehicles is one of the most challenging areas of research. This includes the generation of situations in an automated way, as the resolution of critical situations and emergencies is the basis for safe traffic. We carry out further research in the selection and analysis of suitable formal performance specifications that meet robustness requirements.
Design of controls and preparation of demonstrations related to certain functions of autonomous vehicles:
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