Prof. Dr. Michael Botsch

After completing the module, students are able to:

- explain the mathematical and statistical foundations of uncertainty modeling, distinguish between epistemic and aleatoric uncertainty, and contrast modular and end-to-end AD-stack architectures;
- evaluate methods for quantifying and calibrating uncertainty in perception, identify causes of miscalibration, and select appropriate improvement methods;
- analyze how cooperative V2X systems both reduce and introduce uncertainty, and classify concepts for representing, communicating, and propagating uncertainty in cooperative perception architectures;
- describe human behavior and cognitive models of drivers and VRUs and incorporate them into motion prediction;
- formulate probabilistic models for the motion prediction of traffic participants, trace the propagation of uncertainties along the prediction-to-planning chain mathematically, and apply uncertainty-aware planning methods (e.g., RRT variants);
- assess methods for scenario-based validation, runtime monitoring, and the safe integration of automated driving functions under uncertainty, and judge their suitability against relevance and criticality criteria.

The objective of this course is to give students a broad understanding of the components and algorithms in advanced driver assistance systems and integrated safety.

The objective of this course is to give students a broad understanding of current automotive sensor technologies and of signal processing methods that are used in this field.