Albert Schotschneider: Unterschied zwischen den Versionen
Aus Aifbportal
Kc2837 (Diskussion | Beiträge) |
Kc2837 (Diskussion | Beiträge) |
||
Zeile 11: | Zeile 11: | ||
|Bild=Schotschneider_Albert_small.jpg | |Bild=Schotschneider_Albert_small.jpg | ||
|Info=<br> | |Info=<br> | ||
− | Albert Schotschneider studied computer science and autonomous systems at the Technical University of Darmstadt. Since 2021, he is a research assistant at the FZI Research Center for Information Technology Karlsruhe in the department for Technical Cognitive Systems. His research interests are misbehavior and malfunction detection of driving components with self-optimization capabilities in autonomous driving using machine learning methods. | + | Albert Schotschneider studied computer science and autonomous systems at the Technical University of Darmstadt. Since 2021, he is a research assistant at the FZI Research Center for Information Technology Karlsruhe in the department for Technical Cognitive Systems. His research interests are performance assessment, misbehavior and malfunction detection of driving components with self-optimization and re-training capabilities in autonomous driving using machine learning methods. |
<br> | <br> |
Version vom 4. Februar 2023, 14:12 Uhr
-
M.Sc. Albert Schotschneider
- FZI-Mitarbeiter
- Email: Schotschneider∂fzi de
- Forschungsgruppe: Angewandte Technisch-Kognitive Systeme
-
FZI Forschungszentrum Informatik
Haid-und-Neu-Straße 10-14
76131 Karlsruhe - vCard
Albert Schotschneider studied computer science and autonomous systems at the Technical University of Darmstadt. Since 2021, he is a research assistant at the FZI Research Center for Information Technology Karlsruhe in the department for Technical Cognitive Systems. His research interests are performance assessment, misbehavior and malfunction detection of driving components with self-optimization and re-training capabilities in autonomous driving using machine learning methods.
Open Hiwi Positions
Open Bachelor/Master Theses
- Detecting Mislocalization using Deep Learning Methods for Autonomous Driving [PDF]
- Evaluating Metrics for Performance Assessment in Autonomous Driving [PDF]
Abschlussarbeiten
Forschungsgebiete