explain what autonomy is and what makes a system autonomous.
explain how an autonomous system works and describe important components.
explain optimal control and model predictive control and how they relate to autonomous systems.
explain common methods for visual object detection and tracking (background modelling, appearance-based detectors, region tracking, object tracking).
explain common types of positioning systems (e.g. GNSS, UWB, dead reckoning, inertial navigation, signals of opportunity, magnetic field SLAM), their strengths and weaknesses.
explain common methods and techniques for reinforcement learning, neural networks and deep learning, especially for autonomous systems.