Assuring Safety of AI-based Systems: Lessons Learned for a Driverless Regional Train Case Study

Artificial Intelligence (AI) offers great potential to enable the fully automated operation of trains. Mandatory novel functions to replace the tasks of a human train driver, such as obstacle detection on the tracks, can be realized using state-of-the-art Machine Learning (ML) approaches. However, the use of AI/ML to implement perception tasks in the railway context poses a new challenge: How to link AI/ML techniques with the requirements and approval processes that are applied in the railway domain in practical way? Within the safe.trAIn project we laid the foundation for the safe use of AI/ML to achieve the driverless operation of a regional train. Based on the requirements for the certification process in the railway domain, safe.trAIn investigated methods to develop trustworthiness AI-based functions, taking data quality, robustness, uncertainty, and explainability aspects of the ML model into account. In addition, the project developed a safety argumentation strategy for an AI-based obstacle detection function of a driverless regional train. In this paper, we describe the challenges to assess an AI-based obstacle detection function according to the given regulation in the railway domain. Moreover, we describe our safety assurance strategy applied to our case study in the safe.trAIn project.

Keywords: Driverless regional train, Safety assurance, AI/ML safety, Safety approval, Railway, Autonomous driving.