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 |
ECTS | 3 |
Content
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.