Learning to Annotate Delayed and False AEB Events: A Practical System for Extreme Class Imbalance and Asymmetric Label Noise

Abstract

Autonomous Emergency Braking (AEB) optimization relies on accurately annotated real-world trigger events, particularly rare but critical delayed and false AEB triggers that expose system deficiencies. However, these minority events comprise less than 5% of thousands of daily triggers, making manual annotation prohibitively expensive at scale. We present the first automated AEB annotation framework to address this problem. During development, we identified two fundamental challenge that severely impair delayed/false trigger annotation accuracy: extreme class imbalance where minority events are overwhelmed by true triggers, and asymmetric label noise where mislabeled majority samples suppress minority class learning. To overcome these challenges, we propose two key innovations: (1) Specific data augmentation that synthesizes realistic samples by manipulating focal target attributes, transplanting ego-vehicle dynamics, and masking non-focal agents; (2) Probe-guided noise suppression using stable hardness estimation to clean mislabeled true trigger samples. We deploy our model as a practical annotation system with full-stack architecture, efficiently identifying critical AEB events from thousands of daily samples. Production results demonstrate 80% improvement in delayed/false triggers recall and 50% reduction in manual workload. Beyond immediate gains, the system enables continuous self-improvement through accumulated high-quality annotations, establishing a necessary data foundation for on-vehicle AEB system optimization.