Evaluating autonomous vehicle performance in complex traffic scenarios requires both calibrated sensors and realistic traffic conditions. However, existing traffic datasets focus on vehicle trajectories but lack comprehensive sensor data, while perception datasets provide sensor data but offer limited traffic diversity. To bridge this gap, we present an open-source toolset that generates perception datasets in CARLA by integrating real-world vehicle trajectories into a simulated sensor framework. This approach creates realistic sensor data mirroring real traffic dynamics, providing a valuable resource for testing AI-driven autonomous systems in challenging scenarios like roundabouts, where sensor-based perception is critical for safe navigation.