Towards Simplification of Failure Scenarios for Machine Learning-Enabled Autonomous Systems

Scenario-based testing is an essential way of im-proving the safety and reliability of machine learning-enabled autonomous systems (MLAS), such as autonomous driving systems (ADS). As the complexity of failure scenarios in-creases with the development of more realistic MLAS testing approaches, it becomes essential to simplify failure scenarios to understand and identify the root causes of failures. In this vision paper, we present our vision to leverage search-based software engineering (SBSE) and surrogate-assisted optimisation (SAO) to address the challenges of simplifying failure scenarios in MLAS.