Advanced Driver Assistance Systems (ADAS) validation necessitates large datasets from a variety of driving situations, yet conventional data gathering techniques have serious drawbacks in terms of cost, safety, and scenario coverage. This thesis builds on existing CARLA-based frameworks for generating synthetic data by adding two important new features aimed at improving dataset quality and realism. First, an infrastructure-based sensor simulation system was created, expanding beyond vehicle-mounted sensors to include fixed sensors on traffic lights and road infrastructure, with a dedicated management tool for dynamic sensor placement and configuration while retaining nuScenes dataset compatibility. Second, a novel framework was developed to export complete CARLA simulations to Universal Scene Description (USD) format for integration with NVIDIA Isaac Sim.