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<p>Adverse weather, particularly snowfall and low visibility, severely degrades vision-based traffic perception, yet existing language-driven video segmentation datasets rarely provide pixel-level annotations for real-world driving under such conditions. We present SnowRef-Drive, a large-scale global dataset for instruction-driven traffic video segmentation in adverse winter environments. It contains 19,700 short clips (118,200 annotated frames), each sampled at 2 FPS over 3 seconds (6 frames), paired with a natural language segmentation instruction and frame-wise instance masks. SnowRef-Drive spans 20 winter driving regions across North America, Europe, and East Asia, covering diverse road topologies (urban, highway, mountain/alpine, and mixed transitions) and challenging conditions including heavy snowfall, nighttime snow, fog, and low illumination. The dataset provides a standardized benchmark for evaluating instruction grounding, temporal consistency, and segmentation robustness in safety-critical winter driving scenarios.</p>

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SnowRef-Drive: A Global Instruction-Driven Traffic Video Segmentation Dataset for Adverse Weather Driving Scenarios

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月22日

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  • Senyun Kuang,
  • Yang Liu,
  • Lv Tang,
  • Yintao Wei

摘要

Adverse weather, particularly snowfall and low visibility, severely degrades vision-based traffic perception, yet existing language-driven video segmentation datasets rarely provide pixel-level annotations for real-world driving under such conditions. We present SnowRef-Drive, a large-scale global dataset for instruction-driven traffic video segmentation in adverse winter environments. It contains 19,700 short clips (118,200 annotated frames), each sampled at 2 FPS over 3 seconds (6 frames), paired with a natural language segmentation instruction and frame-wise instance masks. SnowRef-Drive spans 20 winter driving regions across North America, Europe, and East Asia, covering diverse road topologies (urban, highway, mountain/alpine, and mixed transitions) and challenging conditions including heavy snowfall, nighttime snow, fog, and low illumination. The dataset provides a standardized benchmark for evaluating instruction grounding, temporal consistency, and segmentation robustness in safety-critical winter driving scenarios.