Feb. 13, 2024, 5:43 a.m. | Chih-Hong Cheng Paul St\"ockel Xingyu Zhao

cs.LG updates on arXiv.org arxiv.org

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 …

applications beyond collection cost cs.ai cs.lg cs.se data data collection driving fidelity focus future instance modeling role safety safety-critical scalable self-driving self-driving technology synthetic synthetic data technology types visual world

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