Learning to Annotate Delayed and False AEB Events: A Practical System for Extreme Class Imbalance and Asymmetric Label Noise
arXiv:2606.19186v1 Announce Type: cross 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 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
The increasing complexity and deployment of autonomous systems like AEB necessitate advanced methods for validating and improving their performance, especially concerning rare but critical edge cases.
This development addresses a fundamental bottleneck in the scalability and reliability of autonomous systems by automating the annotation of difficult, real-world events, significantly reducing costs and improving safety.
The ability to efficiently annotate delayed and false autonomous emergency braking events allows for faster detection and correction of system deficiencies, accelerating the development and deployment of robust autonomous driving technologies.
- · Autonomous vehicle manufacturers
- · AI/ML annotation platforms
- · Automotive safety regulators
- · Consumers of autonomous vehicles
- · Manual data annotation services
- · Developers relying on limited, human-intensive validation methods
Improved safety and reliability metrics for Autonomous Emergency Braking (AEB) systems.
Accelerated development and broader adoption of autonomous driving technologies as a result of more robust validation processes.
Enhanced trust in AI-driven safety systems, potentially leading to increased regulatory confidence and faster market penetration of advanced robotics.
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Read at arXiv cs.LG