AutoMine Wins CVPR 2026 Scenario Mining Challenge with LLM-VLM Approach
A research team developed AutoMine, an AI system combining large language models (LLMs) and vision language models (VLMs) that won the Argoverse 2 Scenario Mining Competition at CVPR 2026. The system mines safety-critical driving scenarios from large-scale autonomous vehicle logs to improve data-driven evaluation of self-driving systems. This advancement addresses a key challenge in autonomous driving development: identifying the most valuable and safety-relevant scenarios for testing and validation.
AutoMine, a scenario mining method developed by researchers including Songliang Cao and colleagues, achieved top performance in the temporal track of the CVPR 2026 Scenario Mining Challenge, scoring 36.38 on the HOTA-Temporal metric and 77.21 on the Timestamp BA metric. The system uses semantics-preserving prompt augmentation to reduce sensitivity to LLM prompts, combines trajectory atomic functions with VLM-based functions to handle perception noise and visual ambiguities, and refines generated code through execution feedback from real driving logs. Scenario mining from large-scale autonomous driving datasets has become essential for evaluating self-driving systems in a data-driven manner. The approach demonstrates how combining multiple AI modalities—language understanding and visual perception—can improve the extraction of high-value, safety-critical scenarios from real-world driving data. This work contributes to making autonomous vehicle testing more efficient and comprehensive.
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- arXiv cs.AICenter
AutoMine Solution for AV2 2026 Scenario Mining Challenge
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