Safety assurance for automated driving systems that can adapt using machine learning: A qualitative interview study.

Introduction: Automated Driving Systems (ADSs) present significant unresolved challenges for traditional safety assurance frameworks. These frameworks did not envisage, nor readily support, automated driving without the active involvement of a human driver, or support safety-critical systems using Machine Learning (ML) to modify their driving functionality during in-service operation.

Method: An in-depth qualitative interview study was conducted as part of a broader research project on safety assurance of ADSs that can adapt using ML.