视觉语言模型能否感知传感器所感？一种可扩展的专家引导设计用于从街景评估轮椅可达性
Do VLMs See What Sensors Feel? A Scalable Expert-Guided Design for Wheelchair Accessibility Assessment from Street View
AI总结：
本文提出专家引导的检索增强框架，利用视觉语言模型从谷歌街景图像识别轮椅可达性障碍，通过GPS轮椅停留行为验证，表明VLM评分与移动摩擦部分一致，但细粒度障碍识别有限。
摘要：
评估建筑环境交互（如轮椅可达性）是困难的，因为现实世界的移动性受到分布式、上下文依赖和临时性障碍的影响，这些障碍难以大规模捕捉。为了支持可扩展的评估，本文研究了视觉语言模型（VLM）是否能够从谷歌街景（GSV）图像中识别可达性障碍。我们提出了一种专家引导的检索增强框架，结合GSV图像、ADA指导原则和专家制定的评分标准来评估可达性维度。我们在佛罗里达大学收集了一个校园规模的数据集，将407个独特的GSV位置与GPS衍生的轮椅停留行为作为移动摩擦信号相关联。结果表明，VLM评分与停留时间既呈负相关又在分布上相似，表明与移动摩擦的行为代理部分但一致的对齐。视觉线索分析显示，某些环境对象（如路缘坡道和人行横道）与较高的VLM可达性评分相关，而对于细微的表面条件、临时障碍物和视角依赖的障碍，对齐仍然有限。总体而言，我们的发现显示了专家引导的VLM在可扩展的可达性评估中的潜力，与真实世界轮椅导航的传感器衍生指标相一致。
英文摘要：
Assessing built-environment interaction, such as wheelchair accessibility, is difficult because real-world mobility is shaped by distributed, context-dependent, and temporary barriers that are hard to capture at scale. To support scalable assessment, this paper examines whether vision-language models (VLMs) can identify accessibility barriers from Google Street View (GSV) imagery. We propose an expert-guided retrieval-augmented framework that combines GSV images, ADA-informed guidance, and expert-derived rubrics to evaluate accessibility dimensions. We collect a campus-scale dataset at the University of Florida, linking 407 unique GSV locations with GPS-derived wheelchair dwell behavior as a mobility-friction signal. Results show that VLM ratings are both negatively correlated and distributionally similar with dwell time, indicating partial but consistent alignment with a behavioral proxy for mobility friction. Visual cue analysis shows that certain environmental objects, such as curb ramps and crosswalks, are associated with higher VLM accessibility scores, while alignment remains limited for subtle surface conditions, transient obstructions, and viewpoint-dependent barriers. Overall, our findings show the potential of expert-guided VLMs for scalable accessibility assessment aligning with sensor-derived indicators of real-world wheelchair navigation.
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