Road safety assessments are critical yet costly, especially in Low- and Middle-Income Countries (LMICs), where most roads remain unrated. Traditional methods require expert annotation and training data, while supervised learning-based approaches struggle to generalise across regions. In this paper, we introduce \textit{V-RoAst}, a zero-shot Visual Question Answering (VQA) framework using Vision-Language Models (VLMs) to classify road safety attributes defined by the iRAP standard. We introduce the first open-source dataset from ThaiRAP, consisting of over 2,000 curated street-level images from Thailand annotated for this task. We evaluate Gemini-1.5-flash and GPT-4o-mini on this dataset and benchmark their performance against VGGNet and ResNet baselines. While VLMs underperform on spatial awareness, they generalise well to unseen classes and offer flexible prompt-based reasoning without retraining. Our results show that VLMs can serve as automatic road assessment tools when integrated with complementary data. This work is the first to explore VLMs for zero-shot infrastructure risk assessment and opens new directions for automatic, low-cost road safety mapping. Code and dataset: https://github.com/PongNJ/V-RoAst.
@article{arxiv.2408.10872,
title = {V-RoAst: Visual Road Assessment. Can VLM be a Road Safety Assessor Using the iRAP Standard?},
author = {Natchapon Jongwiriyanurak and Zichao Zeng and June Moh Goo and Xinglei Wang and Ilya Ilyankou and Kerkritt Sriroongvikrai and Nicola Christie and Meihui Wang and Huanfa Chen and James Haworth},
journal= {arXiv preprint arXiv:2408.10872},
year = {2026}
}