A cutting-edge, highly automated framework for ADAS validation IAV Caledon is presented, leveraging high-fidelity synthetic data across diverse target environments. The approach is designed to be extendable to include lidar and ultrasonic sensor systems in future developments, combined with realistic multi-sensor stimulation (camera, GNSS) and Vehicle-in-the-Loop and roller dyno integration. This enables reproducible, scenario-based testing under controlled yet realistic conditions. The concept provides deep insights into scalable test catalogue execution, black-box system evaluation, and automated KPI generation—maximizing test coverage, efficiency, and engineering confidence while minimizing manual effort.

• End-to-end ADAS validation via synthetic data & multi-sensor stimulation (ViL, roller dyno)
• Automated test catalogue execution with black-box evaluation & reproducibility
• KPI generation & reporting (e.g. detection rate, pass/fail) aligned to test cases
• Country-specific scenario modeling (roads, signs, localization)
• Highly automated workflows with minimal manual effort and automated driver input