Several machine learning and generative AI technologies are employed in the approach discussed in this TalX to produce high-quality synthetic sensor data for autonomous system training. Real sensor data as initial input ensures focus on target areas and generative AI creates synthetic variation. A novel machine-learning approach extracts relevant custom features from the sensor data sets. These features are used as input to reconstruct realistic 3D training environments. Aspects too detailed to be captured by the sensor data, environmental conditions and dynamic scenarios, are augmented generatively. The workflow allows permutations of any parameter, ensuring high-quality synthetic sensor training data. To make truly autonomous systems as robust as possible, it needs as much training and training data as possible which can only be achieved via synthetic training data. The introduced approach ensures targeted synthetic sensor training generation, aligned with current, realistic, localized, training scenarios to avoid misstraining.