Module RO5500-KP12
Autonomous Vehicles (AVS)
Duration
2 Semester
Turnus of offer
starts every winter semester
Credit points
12
Course of studies, specific fields and terms:
- Master Robotics and Autonomous Systems 2019, advanced curriculum, advanced curriculum
Classes and lectures:
- Technology of Autonomous Vehicles (seminar, 2 SWS)
- 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:
- 60 hours exam preparation
- 220 hours private studies
- 80 hours in-classroom work
Contents of teaching:
- Content of the course Vehicle Dynamics and Control:
- Review of control methods and rigid body dynamics
- Basic terminology of vehicle dynamics
- Vehicle dynamic modeling (lateral, longitudinal, vertical)
- Kinematic and dynamic bicycle model
- Important vehicle components: engine, transmission, brake, steering
- Tire models
- Stability analysis
- Vehicle handling performance
- Suspension systems
- Path planning algorithms
- Active safety systems
- Autonomous driving
- Content of the course Perception for Autonomous Driving:
- Architecture of autonomous-driving systems
- Sensors, signals, systems and tools
- Tracking, detection, fusion, classification, prediction
- Learning theories
- Deep Learning
- Signal estimation and adaptive filters
- Graphical models and dynamic Bayes networks
- Applications in automotive robotics
- Contents of the seminar Current Topics in Autonomous Vehicles:
- Current topics 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.
- Educational objectives of the seminar Current Topics in Autonomous Vehicles:
- Students are able to research and understand current literature.
- Students are able to reproduce and evaluate current algorithms based on research literature.
- Students are able reproduce, extend and present results from current research literature.
Grading through:
- Written or oral exam as announced by the examiner
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:
Admission requirements for taking the module:- None
Admission requirements for participation in module examination(s):
- Successful completion of exercises as specified at the beginning of the semester.
Module Examination(s):
- RO5500-L1: Vehicle Dynamics and Control, written exam, 60min, 50% of module grade
- RO5500-L2: Perception for Autonomous Vehicles, written exam, 60min, 50% of the module grade
- RO5500-L3 Technology of Autonomous Vehicles; Seminar; ungraded; 0% of module grade, must be passed
Last Updated:
18.02.2026