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
Jun 22, 2026
3
min read
Independent Safety Assessment Sets Benchmark for Autonomous Vehicles
Edge Case has completed an independent review of Aurora’s Safety Case, confirming its alignment with industry best practices and reinforcing confidence in the safety of autonomous trucking. The collaboration highlights the growing importance of third-party validation, transparency, and evidence-based safety assurance as autonomous systems scale across real-world operations.
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.
