# Urban Road Anomaly Monitoring Using Vision-Language Models for Enhanced Safety Management
## 1 Introduction
A primary concern is that many machine learning models require substantial volumes of high-quality historical data for effective training [29], which may not always be accessible [30]. Second, traditional AI methods struggle to integrate external prior knowledge, whereas large models employ techniques such as in-context learning (ICL) and retrieval-augmented generation (RAG) to provide real-time prior knowledge, thereby facilitating adaptation to new tasks and improving accuracy [6; 31]. Moreover, these models often lack robustness; changes in camera angles can significantly degrade performance due to variations in the captured images, which limited their abilities in real-time application. This limitation requires the collection of new data in innovative scenarios to fulfill performance metrics. The emergence of next-generation sensors [32] and data-driven technologies [33; 34] has transformed the ways data are collected and interpreted, leading to significant progress in road anomaly monitoring. There is an increasing necessity to investigate more advanced AI models, including visual large language models (VLMs) and various generative AI structures. The rise of generative artificial intelligence represents a significant milestone [35; 36]. These models display outstanding abilities in comprehending human natural language, which allows them to effectively execute tasks across a variety of domains. Trained on progressively larger datasets, large language models reveal exceptional skill in derivinginsightful information from images, even in the absence of specialized training data. Nevertheless, their performance remains constrained in some practical applications [37, 38], underscoring the necessity for additional refinement and customization for specific use cases. Recent research has employed pre-trained generative AI models for the categorization of road anomalies [39]. A vision large language model has been developed for adaptive object detection through interactions in natural language. Evaluations suggest that the VLM framework performs comparably to leading models in road anomaly detection [40]. A GPT-4 model's Python (3.11.10) API was utilized to measure road anomalies and enhance estimation results by referencing specialized materials [41]. A prototype of an autonomous GIS (Geographic Information System) was developed, based on the GPT-4 API, aiming to accept tasks through natural language and autonomously solve spatial problems [42]. These models can offer intelligent solutions that closely resemble human reasoning, allowing us to leverage general artificial intelligence for addressing challenges in a variety of applications. However, there is a need for further improvement in the monitoring models of road anomalies. Given the capabilities of large language models, their potential influence extends across multiple domains, including the real-time safety management of road anomalies that can be explored. This study uses China as a case study and investigates the automatic real-time detection of urban anomalies through vision large language models. It presents three key contributions to the realm of anomaly monitoring and management. Firstly, it employs advanced AI technologies, specifically focusing on tailored vision large language models, to enhance real-time surveillance of anomaly events, thus closing existing methodological gaps. Secondly, by applying innovative modeling approaches, it allows for more accurate assessments of real-time anomalies compared to traditional deep learning models. Lastly, this research aims to educate drivers, policymakers, and stakeholders on effective strategies for managing road surface risks and promoting environmental sustainability. It aids in the development of more resilient urban infrastructure that can respond to evolving climate changes while enhancing overall resilience. In this paper, we propose anomalies monitoring vision large language models, referred to as Urban Road Anomaly Visual Large Language Models (URA-VLMs).