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 ensure that applying testing on synthetic data can reveal real-world safety issues, and the absence of safety-critical issues when testing under synthetic data can provide a strong safety guarantee in real-world behavior. We suggest an optimization method to refine the synthetic data generator, reducing fidelity gaps identified by deep learning components. Experiments show this tuning enhances the correlation between safety-critical errors in synthetic and real data.
@article{arxiv.2402.07031,
title = {Instance-Level Safety-Aware Fidelity of Synthetic Data and Its Calibration},
author = {Chih-Hong Cheng and Paul Stöckel and Xingyu Zhao},
journal= {arXiv preprint arXiv:2402.07031},
year = {2025}
}