Traditional automation technologies alone are not sufficient to enable the fully automated operation of trains. However, Artificial Intelligence (AI) and Machine Learning (ML) offers great potential to realize the mandatory novel functions to replace the tasks of a human train driver, such as obstacle detection on the tracks. The problem, which still remains unresolved, is to find a practical way to link AI/ML techniques with the requirements and approval processes that are applied in the railway domain. The safe.trAIn project aims to lay the foundation for the safe use of AI/ML to achieve the driverless operation of rail vehicles and thus addresses this key technological challenge hindering the adoption of unmanned rail transport. The project goals are to develop guidelines and methods for the reliable engineering and safety assurance of ML in the railway domain. Therefore, the project investigates methods to reliable design ML models and to prove the trustworthiness of AI-based functions taking robustness, uncertainty, and transparency aspects of the AI/ML model into account.

Marc Zeller works as a Senior Key Expert for model-based safety and reliability engineering at Siemens Technology in Munich. His research interests are focused on the efficient and effective development of dependability-relevant Cyber-physical Systems as well as the assurance trustworthness of AI-based systems using model-based engineering techniques. Moreover, he is co-lead of the Siemens wide research program “Engineering and Validation of Intelligent Systems”. Marc Zeller received a diploma in Computer Science from the Karlsruhe Institute of Technology (KIT) in 2007 and obtained a PhD in Computer Science from the University of Augsburg in 2013. With over 10-years’ experience in different industrial domains, such as automotive, railway, avionics, or industry automations, he has been involved in various indutrial projects establishing model-based engineering techniques and is author of over 70 international publications in this area.