Module RO5500-KP08

Duration

2 Semester

Turnus of offer

each semester

Credit points

08

Course of studies, specific fields and terms:

• Master Robotics and Autonomous Systems 2019, optional subject, Elective

Classes and lectures:

• Perception for Autonomous Vehicles (exercise, 2 SWS)
• Perception for Autonomous Vehicles (lecture, 2 SWS)
• Vehicle Dynamics and Control (exercise, 2 SWS)
• Vehicle Dynamics and Control (lecture, 2 SWS)

Workload:

• 40 hours exam preparation
• 140 hours private studies
• 60 hours in-classroom work

Contents of teaching:

• Content of teaching of the course Vehicle Dynamics and Control:
• Review of control methods and rigid body dynamics
• Basic terminology of vehicle dynamics
• Vehicle dynamic models (lateral, longitudinal, vertical)
• Component models (engine, transmission, brake, steering)
• Tire modeling
• Stability analysis
• Handling performance
• Active safety systems
• Autonomous driving
• Content of teaching of the course Perception for Autonomous Driving:
• The architecture of autonomous-driving systems
• Tracking, detection, classification
• Models of stochastic signals
• Transform-based analysis of stochastic signals
• System theory
• Parameter estimation
• Linear optimal filters and adaptive filters
• Graphical models and dynamic Bayes networks
• Neural networks
• Hidden Markov Models, Kalman Filter, Particle Filter, etc.
• Applications in the domain of autonomous driving
• Content of teaching of the seminar Current Topics in Autonomous Vehicles:
• Current algorithms in machine learning and artificial intelligence related to autonomous driving

Qualification-goals/Competencies:

• Educational objectives of the course Vehicle Dynamics and Control:
• Students master basic terminology and concepts of vehicle dynamics.
• Students obtain a comprehensive understanding of the dynamics of a vehicle.
• Students understand the main objectives of vehicle control.
• Students can derive basic vehicle dynamics models for control design.
• Students are able to apply concepts of basic and advanced control and estimation to practical problems.
• Students get an insight into the field of active safety systems, driver assistance, and autonomous driving.
• Students are able to perform independent design, research and development work in this field.
• Educational objectives of the course Perception for Autonomous Driving:
• Students get an overview on autonomous-driving systems.
• Students become thoroughly acquainted with the perception layer of the architecture of an autonomous-driving system.
• Students get a comprehensive introduction to stochastic signals.
• Students master tools for the analysis of stochastic signals.
• Students are able to make use of various models for stochastic signals.
• Students are able to design tracking algorithms.
• Students are able devise algorithmic solutions to decision problems, while making use of prior knowledge.

Grading through:

• Written or oral exam as announced by the examiner

Requires:

• Control Systems (RO4400-KP08)
• Technical Mechanics (RO1500-KP08)

Responsible for this module:

• Prof. Dr. Georg Schildbach

Teacher:

• Institute for Electrical Engineering in Medicine
• Prof. Dr. Georg Schildbach
• PD Dr.-Ing. habil. Alexandru Paul Condurache

Literature:

• Rajamani, R : Vehicle Dynamics and Control (2nd edition) Springer, 2012, ISBN 978-1-4614-1432-2
• Mitschke, M; Wallentowitz, H. : Dynamik der Kraftfahrzeuge (5th edition) Springer, 2014 (ISBN: 978-3-658-05067-2)
• Charles W. Therrien : Decision estimation and classification J. Wiley and Sons, 1991.
• Simon Haykin : Adaptive Filter Theory Prentice Hall, 1996
• Christopher M. Bishop : Pattern recognition and machine learning Springer, 2006
• A. Mertins : Signaltheorie: Grundlagen der Signalbeschreibung, Filterbänke, Wavelets, Zeit-Frequenz-Analyse, Parameter- und Signalschätzung Springer-Vieweg, 3. Auflage, 2013

Language:

• offered only in English

Notes:

Last Updated:

18.02.2026