Continuous Development and Safety Assurance Pipeline for ML-based Systems in the Railway Domain
This paper details the implementation of a Machine Learning Operations (MLOps) process for Automated Driving Systems (ADS) in the railway domain. It addresses the challenges of applying machine learning in safety-critical railway systems and outlines a comprehensive approach to continuous development and safety assurance of ML-based systems. The paper emphasizes the use of Git-centric methods and appropriate tooling to automate the process and ensure safety.
Download the full whitepaper to understand how to implement a safe MLOps process, including data quality assurance, ML model development, and safety case management, ensuring the trustworthiness of AI-based functions in driverless trains.
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CONTENT TYPE
Mar 2, 2026
2
min read
Creating A Defensible Safety Foundation: American Rheinmetall + Edge Case Partnership Story
What began as targeted safety support has grown into a trusted partnership. Edge Case provides American Rheinmetall with flexible consulting, structured safety frameworks, and ongoing guidance that supports critical program reviews and long-term safety maturity.
CONTENT TYPE
Nov 14, 2025
3
min read
Edge Case Recognized as a 2025 Top Autonomous Vehicle Safety System by AutoTech Outlook
Edge Case was selected by AutoTech Outlook’s readers for a peer nominated award recognizing our leadership in autonomous vehicle safety and the impact of our work across frontier technologies.
