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AutoMine method uses LLMs and VLMs for autonomous driving scenario mining

By PulseAugur Editorial · [2 sources] ·

Researchers have developed AutoMine, a novel method for extracting critical scenarios from autonomous driving data using Large Language Models (LLMs) and Vision-Language Models (VLMs). This approach enhances prompt sensitivity reduction and integrates trajectory functions with VLM capabilities to manage perception noise and visual cues. AutoMine refines generated code through feedback from real-world log executions, achieving strong performance in the Argoverse 2 Scenario Mining Competition. AI

IMPACT This method could improve the safety and efficiency of autonomous driving systems by enabling better data-driven evaluation.

RANK_REASON The cluster contains an academic paper detailing a new method for scenario mining in autonomous driving.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · 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 ·

    AutoMine Solution for AV2 2026 Scenario Mining Challenge

    arXiv: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 AutoMin…

  2. arXiv cs.AI TIER_1 English(EN) · Hangjun Ye ·

    AutoMine Solution for AV2 2026 Scenario Mining Challenge

    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…

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