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arXiv · 2402.07031

Instance-Level Safety-Aware Fidelity of Synthetic Data and Its Calibration

Chih-Hong Cheng·Paul Stöckel·Xingyu Zhao
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Abstract

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.

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Chih-Hong Cheng, Paul Stöckel, Xingyu Zhao. 2024-05-02. Instance-Level Safety-Aware Fidelity of Synthetic Data and Its Calibration. https://doi.org/10.1109/itsc58415.2024.10920032

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