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arXiv · 2606.11874

AutoMine Solution for AV2 2026 Scenario Mining Challenge

Songliang Cao·Jiele Zhao·Yuru Wang·Hao Li·Daqi Liu·Zehan Zhang·Fangzhen Li·Yu Wang·Yue Zhang·Bing Wang·Guang Chen·Hao Lu·Hangjun Ye
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Abstract

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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Songliang Cao, Jiele Zhao, Yuru Wang, Hao Li, Daqi Liu, Zehan Zhang, Fangzhen Li, Yu Wang, Yue Zhang, Bing Wang, Guang Chen, Hao Lu, Hangjun Ye. 2026-06-10. AutoMine Solution for AV2 2026 Scenario Mining Challenge. https://arxiv.org/abs/2606.11874

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