AutoMine Solution for AV2 2026 Scenario Mining Challenge
Quick Answer
This paper shows that AutoMine, a novel scenario mining method leveraging LLMs and VLMs, excels in the Argoverse 2 Scenario Mining Competition with a HOTA-Temporal score of 36.38 and a Timestamp BA score of 77.21, addressing the need for high-value, safety-critical scenario extraction from driving logs.
Key Points
- AutoMine reduces prompt sensitivity using semantics-preserving prompt augmentation.
- Combines trajectory atomic functions with -based functions to manage perception noise.
- Refines generated code through execution feedback from real driving logs.
- Achieved notable scores in the CVPR 2026 competition, enhancing autonomous driving evaluation.
Paper Resources
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~2 min readAuthors: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
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.
| Comments: | CVPR 2026 Scenario Mining Challenge (Temporal Track Winners) |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.11874 [cs.AI] |
| (or arXiv:2606.11874v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.11874 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hao Li [view email]
[v1]
Wed, 10 Jun 2026 09:58:21 UTC (342 KB)
— Originally published at arxiv.org
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