Advanced Driver Assistance Systems (ADAS) are instrumental in improving road safety and minimizing traffic-related incidents. However, their development and validation processes are resource-intensive, requiring substantial time, cost, and domain-specific expertise. Moreover, real-world testing introduces significant safety challenges. To address these issues, virtual simulation platforms offer high-fidelity environments for the secure and efficient testing of ADAS functions. This research presents a virtual validation framework for a Traffic Jam Pilot (TJP) algorithm utilizing such simulators. The framework features detailed models of camera and radar sensors, capturing essential parameters like detection range and field of view, alongside a vehicle plant model and road infrastructure modeling that includes elements such as curvature, slope, banking angles, and varying lane widths. A perception stack is developed using synthetic sensor data and is integrated with the TJP control algorithm to manage the Ego vehicle in dynamic traffic scenarios, including stop-and-go and cut-in maneuvers. The approach enables comprehensive system evaluation in a risk-free environment, significantly reducing development complexity and cost. A key contribution of this work is the generation of virtual test scenarios derived from real-world driving data, allowing for direct, scenario-specific comparisons between simulation outputs and physical-world behavior. The findings underscore the potential of simulation-based validation as a scalable and reliable pathway toward deploying ADAS functions with improved safety and efficiency.