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原文摘要

Potholes impose billions of dollars in annual vehicle damage, yet detectors report only where potholes are, not how severe they are or what to do. Judging severity from a single RGB image is ill-posed, and ungrounded vision-language model (VLM) assessments tend to be over-conservative. We present a five-stage modular framework for the offline assessment of already-detected potholes that combines geometric measurements with VLM reasoning: perspective transformation, zero-shot SAM segmentation, geometric feature extraction, Gemini 2.5 Pro severity classification with composite risk scoring, and natural-language warning generation. We evaluate it on 3,092 potholes in 1,119 dashcam scenes against independent references. Severity agreement with a prediction-free three-annotator panel is weak (Cohen's $κ= 0.139$, 95% CI [0.00, 0.28]; $n = 101$) and far below the annotators' agreement with one another ($κ= 0.605$); against physically measured depth on an independent RGB-D dataset, it is also weak ($κ= 0.098$), although the predicted classes preserve the order of measured depths. The severity classes are therefore perceived visual categories, not depth measurements. Geometric grounding does change the warnings: relative to an image-only baseline, it moves 26.7% of scenes out of the high-urgency tier and raises critical-tier assignments by 109% (Stuart-Maxwell $p < 10^{-15}$), and in a blind study, three raters preferred the grounded warnings 3.5:1. A six-arm prompt ablation attributes the hybrid prompt's rule adherence to the per-pothole measurements rather than to the rubric or worked examples. At USD 0.105 per scene, it suits large-scale offline infrastructure assessment; its maneuver suggestions are advisory research outputs, not validated vehicle guidance.