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arXiv Machine Learning By Zewei Li, Qiaoqiao Ren, Hang Yang, S. C. Wong, Stergios-Aristoteles Mitoulis, Yun Ye

Not All Synthetic Data Are Equal: Expert-Committee Audit Screening for Imbalanced Crash-Injury-Severity Prediction in Automated Driving Systems

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The paper introduces Expert-Committee Audit Screening (ECAS), a framework that evaluates the credibility of synthetic minority samples for predicting crash injury severity in automated driving systems. Using real incident data from the NHTSA, ECAS filters generated samples based on label support, boundary separation, committee agreement, and local plausibility, then selects accepted samples via within‑class percentile normalization and Pareto non‑dominated sorting. The best ECAS configuration, combined with normalizing flow augmentation and a TabPFN classifier, outperformed other evidence settings in balanced accuracy, macro‑F1, and minor‑injury recall, and analysis showed ECAS‑accepted samples were better supported by nearby real crashes.

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