Automated Driving

Since the winter term 2021/2022, the new lecture Automated Driving is offered at the ris. The lecturer is our alumnus Dr.-Ing. Matthias Schreier

Lecture V2
Date look at Tucan
Location look at Tucan
Language english
Lecturer Dr.-Ing. Matthias Schreier
Date look at Tucan
Location look at Tucan
Contact Person Dr.-Ing. Matthias Schreier
Allowed Tools none
Exam relevant contents Content of the lecture Automated Driving
Exam type in writing
Exam duration 90 minutes
Exam view to be announced
Important Information considering 2G wristbands


In the lecture Automated Driving, content from the following areas is taught:

  • 1. Motivation & History
  • 2. Terminology & Paths Towards Automated Driving
  • 3. Building Blocks, Hardware, and Components
  • 4. Vehicle Environment Models
  • 5. Object Detection & Semantic Segmentation
  • 6. Data Fusion & State Estimation
  • 7. Bayesian Inference & Kalman Filtering
  • 8. Target Tracking & Traffic Participant Fusion
  • 9. Grid Mapping & Free Space Estimation
  • 10. Localization & High-Definition Maps
  • 11. Situation Understanding & Prediction
  • 12. Planning & Decision Making
  • 13. Motion Control

Materials and Literature

Lecture slides are distributed in advance of any lecture. For more detailed insights into the topic area, the following books can be recommended:

  • Eskandarian, A.: Handbook of Intelligent Vehicles. Springer, London, 2012.
  • Siciliano, B.; Khatib, O.: Springer Handbook of Robotics. 2nd Edition, Springer, Berlin Heidelberg 2016.
  • Thrun, S.; Burgard, W.; Fox, D.: Probabilistic Robotics. Intelligent Robotics and Autonomous Agents. The MIT Press, Cambridge, 2006.
  • Watzenig, D.; Horn, M.: Automated Driving. Safer and More Efficient Future Driving. Springer, Switzerland, 2017.
  • Winner, H. et al.: Handbook of Driver Assistance Systems. Basic Information, Components and Systems for Active Safety and Comfort. Springer, Switzerland, 2016.