Not All Synthetic Data Are Equal: Expert-Committee Audit Screening for Imbalanced Crash-Injury-Severity Prediction in Automated Driving Systems
Automated driving systems (ADSs) are increasingly operating on public roads, raising safety concerns, yet reliable prediction of crash injury severity remains difficult because crash reports are limited, severe outcomes are rare, and injury classes are highly imbalanced. Existing augmentation methods mainly increase minority-class sample size but rarely assess whether generated samples are credible for safety-critical prediction. This study proposes Expert-Committee Audit Screening (ECAS), a credibility-aware sample acceptance framework for ADS crash injury severity prediction under data imbalance. Using 1,477 incident-level ADS crashes from the National Highway Traffic Safety Administrat...
| Authors | Zewei Li, Qiaoqiao Ren, Hang Yang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 30 August 2026 |
| Last updated at source | 30 August 2026 |
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