Instance-Level Safety-Aware Fidelity of Synthetic Data and Its
  Calibration
Chih-Hong Cheng Paul Stöckel Xingyu Zhao
Modeling and calibrating the fidelity of synthetic data is paramount in
shaping the future of safe and reliable self-driving technology by offering a
cost-effective and scalable alternative to real-world data collection. We focus
on its role in safety-critical applications, introducing four types of
instance-level fidelity that go beyond mere visual input characteristics. The
aim is to align synthetic data with real-world safety issues. We suggest an
optimization method to refine the synthetic data generator, reducing fidelity
gaps identified by the DNN-based component. Our findings show this tuning
enhances the correlation between safety-critical errors in synthetic and real
images.