Instance-Level Safety-Aware Fidelity of Synthetic Data and Its Calibration
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915242164879360 |
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| author | Cheng, Chih-Hong Stöckel, Paul Zhao, Xingyu |
| author_facet | Cheng, Chih-Hong Stöckel, Paul Zhao, Xingyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_07031 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Instance-Level Safety-Aware Fidelity of Synthetic Data and Its Calibration Cheng, Chih-Hong Stöckel, Paul Zhao, Xingyu Software Engineering Artificial Intelligence Machine Learning 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. |
| title | Instance-Level Safety-Aware Fidelity of Synthetic Data and Its Calibration |
| topic | Software Engineering Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2402.07031 |