自动标注罕见但关键的AEB误触发事件，解决数据不平衡与标签噪声问题。
Learning to Annotate Delayed and False AEB Events: A Practical System for Extreme Class Imbalance and Asymmetric Label Noise
- 通过模拟目标属性、车辆动态和遮蔽非焦点对象来生成真实样本。
- 识别延迟/误触发事件召回率提升80%，人工标注工作量减少50%。
- 适合自动驾驶系统优化团队，可持续积累高质量标注数据。
自动驾驶紧急制动（AEB）优化依赖于对真实世界触发事件的精确标注，尤其是稀少但关键的延迟和误触发事件，这些事件暴露了系统的缺陷。然而，这些少数样本占每日数千次触发事件的不足5%，导致人工标注在规模上成本过高。本文提出首个自动化AEB标注框架。开发中发现两大根本挑战：（1）极端类别不平衡，延迟/误触发事件被大量真实触发事件淹没；（2）非对称标签噪声，错误标注的多数样本（真实触发）抑制了少数样本（延迟/误触发）的学习。为此，我们提出两项创新：（1）特定数据增强，通过操纵焦点目标属性、移植本车动态、遮蔽非焦点代理生成真实样本；（2）利用稳定难度估计与探针引导自适应阈值进行噪声抑制，清理错误标注的真实触发样本。关键的是，我们将模型部署为全栈式实用标注系统，高效从每日数千个AEB事件中识别关键延迟/误触发事件。生产结果表明，延迟/误触发事件召回率提升80%，人工工作量减少50%。此外，系统可通过累积高质量标注实现持续自我优化，为车载AEB系统优化建立必要数据基础。
原文摘要 · Abstract (English)
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 samples 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 challenges that severely impair delayed/false trigger annotation accuracy: (1) Extreme class imbalance where delayed/false triggers are overwhelmed by true triggers; (2) Asymmetric label noise where mislabeled majority samples (true triggers) suppress minority samples (delayed/false triggers) 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) noise suppression using stable hardness estimation and probe-guided adaptive threshold to clean mislabeled true trigger samples. Crucially, we deploy our model as a practical annotation system with full-stack architecture, efficiently identifying critical delayed/false triggers from thousands of daily AEB events. Production results demonstrate 80% improvement in recall of delayed/false triggers 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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。