SafeTrAIn
Project Members: Konstantin Kirchheim
Project Time: 01.01.2022-31.12.2024
Funded By: Federal Ministry for Economic Affairs and Climate Action
Machine Learning (ML) is currently viewed as the most promising approach for autonomous driving. In safety-critical contexts, like autonomous rail vehicles, ML models have to be provenly robust in order to, for example, detect vehicles on roads reliably. The project SafeTrAIn aims to set the foundation for the safe deployment of ML models in autonomous rail vehicles, thereby taking steps towards utilizing ML models in autonomous vehicles in general.
Goals
- Development of solutions that allow to prove the safety of machine learning-based autonomous rail vehicles
- Development of architectures and methods for the safety concept
- Derivation of relevant criterions for the safety proof
- Development of concepts for the fast and efficient constuction of GoA4-Rail vehicles.
Partners:
- Siemens AG
- Siemens Mobility
- Fraunhofer
- Merantix Labs
- Edge Case Research
- Bit technology Solutions
- Setlabs Research
- Bridgefield
- University Applied Science Düsseldorf
- TÜV Rheinland InterTraffic
- TÜV Süd Rail
- TÜV Nord
- German Institute for Standardization (DIN)
- VDE
- BSI
- ITQ
If you are interested in this area of research and want to gather some first hand experience (in the form of a thesis, a team project, a scientific project or a hiwi) please contact Konstantin Kirchheim.
