An automated AEB annotation framework uses data augmentation and noise suppression to achieve 80% recall improvement and 50% workload reduction for rare delayed/false triggers under class imbalance and asymmetric label noise.
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Learning to Annotate Delayed and False AEB Events: A Practical System for Extreme Class Imbalance and Asymmetric Label Noise
An automated AEB annotation framework uses data augmentation and noise suppression to achieve 80% recall improvement and 50% workload reduction for rare delayed/false triggers under class imbalance and asymmetric label noise.