Exam Results

• Exam Results 30.09.2024

Overview

Automated driving is rapidly evolving from basic assistance systems, like ABS and ESP, to advanced autonomous systems that will reshape how we interact with vehicles. The future of driving lies in the seamless integration of advanced technologies that take over tasks such as steering, braking, and acceleration, providing new standards of safety and comfort.

This course explores the exciting transition from driver support to full vehicle automation, focusing on how intelligent systems can make real-time decisions to ensure smooth, safe, and predictable driving even in complex, uncertain environments. Along the way, it introduces AI-driven techniques like reinforcement learning and imitation learning, which are key to enhancing decision-making capabilities.

If you're passionate about cutting-edge automotive technology and want to understand how vehicles of tomorrow will think, plan, and navigate, this course will give you the tools to dive into the world of autonomous driving.

Content

1. Assistance systems with active driving support:
   1.   Introduction to driver assistance
   2.   System description and modeling
   3.   Assistance systems at the stabilization level
   4.   Assistance systems at the guidance level
2. Real-time maneuver optimization:
   1.   Introduction maneuver planning
   2.   Dynamic programming
   3.   Linear-quadratic optimization problems
   4.   Model predictive control
3. Decision making under uncertainty:
   1.   Markov decision processes
   2.   Reinforcement learning
   3.   Imitation learning

Prerequisites

Ideally, you have previously attended “Measurement and Control Systems/Grundlagen der Mess- und Regelungstechnik” or basic knowledge of measurement and control systems and system theory from a lecture from other faculties.