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.
Quick Take
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 LLM prompt sensitivity using semantics-preserving prompt augmentation.
- Combines trajectory atomic functions with VLM-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
Article Excerpt
From source RSS / original summaryarXiv:2606. 11874v1 Announce Type: new 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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