In recent years, the development of autonomous vehicles has necessitated advanced testing methods to ensure safety and reliability. This paper explores the challenges associated with the testing of autonomous cyber-physical systems, particularly those that rely on vision-based machine learning components. Traditional testing methods, such as real-world driving tests, are expensive and time-consuming, requiring vast amounts of data to achieve statistically significant results. To address these challenges, we propose the combination of a graph-based modeling tool utilizing partial model refinement and simulation-based testing approaches.

By generating synthetic sensor inputs within dynamic driving simulators, we can create diverse and complex test scenarios that mimic real-world conditions. The research highlights the importance of effective graph pattern matching and scalable graph generation as essential components for enhancing the robustness of testing strategies. Furthermore, we present a framework for evaluating the effectiveness of these testing methods, contributing to the ongoing efforts to establish reliable and safe autonomous driving solutions.