This paper presents an innovative approach toward data-driven scenario synthesis for verification of active safety functions. Despite the recent rapid growth of active safety to consolidate road safety, they still have various challenges coping with dynamic traffic situations of daily life. Therefore, automotive systems Engineering has established data- and knowledge-driven test methods to assure the required software reliability in a highly safety-critical context. However, the reliance on Naturalistic-Field Operational Tests (N-FOT) is inadequate and, in particular, time and cost-intensive when applied to the next generation of automated driving functions, e.g. advanced emergency braking and truck platooning. The presented framework utilises an ontology-based test scenario synthesis to identify criticality margins using a Cluster-Hardware-in-the-Loop (Cluster-HiL) co-simulation platform. Additionally, we demonstrate a systematic process to complement virtual testing by extracting insights from N-FOT database using event-based time series analysis. To this end, data mining techniques are used to obtain representative scenarios witnessed in real-world traffic. Hierarchical Agglomerative Clustering (HAC) is performed to extract homogeneous groups (clusters) from recorded trigger-events by defined similarity metrics. Extracted scenarios are subsequently used at earlier stages of development to effectively and efficiently ensure reliability and safety. In summary, the results show advantages and some of the challenges faced in the industry using the industry-proven framework, which makes the way to improve testability of the Electronic Control Unit (ECU) and to reduce the expenses of stochastic road tests throughout the software product engineering.