Defect-based Testing for Safety-critical ML Components
Self-driving cars have the potential to revolutionize transportation, but ensuring their safety is paramount. A key challenge lies in guaranteeing "Safety of the Intended This white paper introduces a novel process for collecting adequate test data for machine learning (ML) components, drawing inspiration from defect-based software testing. It addresses the challenge of testing ML components in safety-critical applications, where the input space is complex and exhaustive testing is impractical. We propose a systematic approach that leverages ML data quality metrics and methods to enhance test data and uncover critical scenarios. The effectiveness of the proposed process is demonstrated through case studies involving stop sign recognition and railway track segmentation.
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Mar 2, 2026
2
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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.
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Nov 14, 2025
3
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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.
