mlsanityData to 14 Oct 2026

AutoMine Solution for AV2 2026 Scenario Mining Challenge

arXiv 2606.11874 ↗First posted 10 Jun 2026cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 6 references that are in the map. First posted on arXiv 10 Jun 2026.

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5 of 6 references.

References · 5

  1. 2023Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formattingcited by 817
  2. 2023Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecastingcited by 707
  3. 2021Immortal Tracker: Tracklet Never Diescited by 42
  4. 2025RefAV: Towards Planning-Centric Scenario Miningcited by 13
  5. 2023Technical Report for Argoverse Challenges on Unified Sensor-based Detection, Tracking, and Forecastingcited by 8

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  1. 2026SMc2f: Robust Scenario Mining for Robotic Autonomy from Coarse to Fine
  2. 2025Seeking to Collide: Online Safety-Critical Scenario Generation for Autonomous Driving with Retrieval Augmented Large Language Models
  3. 2025From Failures to Fixes: LLM-Driven Scenario Repair for Self-Evolving Autonomous Driving
  4. 2025Driving-RAG: Driving Scenarios Embedding, Search, and RAG Applications
  5. 2025From Words to Collisions: LLM-Guided Evaluation and Adversarial Generation of Safety-Critical Driving Scenarios
  6. 2023ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling
  7. 2025A Tool for Benchmarking Large Language Models' Robustness in Assessing the Realism of Driving Scenarios ↗
  8. 2025Learning from Risk: LLM-Guided Generation of Safety-Critical Scenarios with Prior Knowledge