Ensuring the safety and functionality of automated vehicles (AVs) is critical in their validation and deployment. Traditional real-world testing is costly, time-consuming, and often lacks reproducibility. To address these challenges, scenario-based testing has emerged as an effective approach, allowing controlled and repeatable evaluations of AV performance under diverse conditions. This thesis presents a novel methodology for combining scenario elements to generate complex, realistic testing environments. The study proposes a structured framework for merging driving scenarios by analyzing scenario similarity and integrating elements from multiple real-world datasets. Using real-world traffic recordings from inD Maps and OMEGA files, the research identifies similar driving scenarios and replaces ego vehicle trajectories to create new, dynamic test cases.