用大模型自动挖掘自动驾驶高危场景，提升安全评估效率。
AutoMine Solution for AV2 2026 Scenario Mining Challenge
- 结合大模型与视觉模型，自动生成并优化场景挖掘代码。
- 在Argoverse 2竞赛中达36.38的HOTA-Temporal得分，77.21的时序对齐分。
- 适合自动驾驶数据评估、场景挖掘研究者使用。
随着自动驾驶系统的发展，从大规模驾驶日志中挖掘高价值、安全关键且与规划相关的情景，已成为数据驱动评估的关键。本文提出AutoMine，一种基于大语言模型（LLMs）和视觉语言模型（VLMs）的鲁棒自迭代场景挖掘方法。AutoMine采用语义保持的提示增强策略，降低大模型对提示的敏感性；结合鲁棒轨迹原子函数与基于VLM的功能模块，有效应对感知噪声和开放世界视觉线索；并通过真实日志执行反馈不断优化生成代码。在CVPR 2026举办的Argoverse 2情景挖掘竞赛中，AutoMine取得36.38的HOTA-Temporal得分和77.21的时序对齐（Timestamp BA）得分。
原文摘要 · Abstract (English)
With the development of autonomous driving systems, mining high-value, safety-critical, and planning-relevant scenarios from large-scale driving logs has become essential for data-driven evaluation. In this paper, we propose AutoMine, a robust self-refining scenario mining method based on LLMs and VLMs. AutoMine uses semantics-preserving prompt augmentation to reduce LLM prompt sensitivity, combines robust trajectory atomic functions with VLM-based functions to handle perception noise and open-world visual cues, and refines generated code through execution feedback from real logs. In the Argoverse 2 Scenario Mining Competition at CVPR 2026, AutoMine achieves a HOTA-Temporal score of 36.38 and a Timestamp BA score of 77.21.
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