# Urban Road Anomaly Monitoring Using Vision-Language Models for Enhanced Safety Management
## 4 Discussion
Consequently, zero-shot learning facilitates the transfer of attributes learned from visible classes to invisible classes, enabling the classification of invisible classes without the need for labeled samples. This approach warrants exploration for the long-term usability of garbage classification [62]. Moreover, a distributed framework based on federated learning [63] and a novel three-stage method utilizing Ensemble ELM [64] have demonstrated accuracy exceeding 90% on targeted datasets. The accuracy of image recognition can be significantly improved for specific types of waste. However, in scenarios involving mixed types of waste, further investigation is still required. These URA-VLMs would be applied in road monitoring or autonomous driving. We conducted tests using lightweight Wi-Fi cameras. In the tests on the A800 cloud server, the response time for one image was an average of 9.3 s. On the edge cloud server, the response time was 12.1 s. These tests verified the feasibility of the system and model design. However, in practical applications, the complexity of the scenarios will still affect the accuracy. Further design and optimization in terms of response time, privacy, and ethics are required. One crucial aspect demanding particular emphasis is privacy. While AI brings certain privacy advantages, it also presents challenges [65]. In highly recognizable scenarios, AI can assist us in swiftly identifying risks and desensitizing data. However, in numerous areas that remain underexplored, AI harbors long-term latent risks yet to be identified. At least three key issues, like vehicle authorization, privacy protection, and report source validation, should be highlighted in the road anomalies monitoring [66]. AI methods, such as the FDMME, have been utilized for privacy preservation in the detection of road anomaly conditions [67]. The hybrid approach combining federated learning, differential privacy, and homomorphic encryption has been employed to maximize user data privacy without compromising the overall accuracy of the AI model [68]. Moreover, there are also non-AI methods for privacy protection. One such approach is a traffic route management scheme that employs homomorphic encryption and blockchain technology, achieving security objectives and demonstrating good performance [69]. A novel Vehicular Flexible Anonymous System (VFAS) has been developed, enabling vehicles to autonomously generate pseudonyms and keys for enhanced privacy and memory efficiency [70]. AI methodologies provide numerous effective solutions for privacy protection. At the same time, non-AI techniques protect data from various perspectives, including cross-validation and secure data transmission. A wide array of strategies is available. In light of the ethical risks, it is essential to ensure that algorithm designs adhere to ethical standards. However, a unified ethical standard encompassing transparency, fairness, and privacy has yet to be established [71]. A thorough assessment of the possible dangers that might affect both individuals and society needs to be conducted, along with the formulation of proactive strategies. A complete framework should be established to monitor the full progression of AI development, assessment, and implementation. This includes training staff and enhancing privacy safeguards and ethical evaluation processes. For generative AI, further institutional advancements are essential [72; 73]. This study presents a rapid, convenient, and innovative generative model for real-time road anomaly monitoring; however, several limitations should be addressed. First,while we focused on the InternVL model, there is significant potential to explore other models in related monitoring tasks with different data sources that could further enhance performance [40,74]. Second, our research utilized a small sample size without fine-tuning, indicating that additional fine-tuning with larger datasets may lead to improvements in model accuracy.