## 5 Conclusions and Discussion  
In this article, the ability of AI, particularly the K-means clustering algorithm, to address the challenges encountered by VANETs is examined. The application of K-means clustering serves to augment both the efficiency and reliability of communications by means of clustering methodologies, thereby enhancing the exchange of information among vehicles. The proposed integrated ITS framework, which leverages both VANETs and AI techniques, aspires to amplify data throughput while simultaneously diminishing response times, ultimately fostering enhanced safety features. Furthermore, this initiative aligns with the 2030 objectives established by the United Nations to reduce the mortality rate resulting from road accidents, which will improve healthcare, signifying that substantial advancements have been achieved thus far. In addition, AI-driven solutions pertaining to VANETs continue to proliferate, with vehicle communication and security enhancements being of paramount concern during this critical global juncture.  
Furthermore, the methodology that has been put forth is heavily dependent on the establishment of V2V communication, which necessitates that each and every vehicle involved in this intricate system effectively communicates with all other vehicles, thereby fostering a collaborative network among them. To achieve such seamless communication, it becomes imperative for all vehicles that are part of the ITS framework to be equipped with specialized technological apparatus, which consequently leads to a significant escalation in the overall financial expenditure associated with the implementation of this system. This financial burden can be alleviated through the introduction of an incentive-based mechanism that could be initiated by insurance providers, a strategy that not only serves to enhance the understanding of driver behavior patterns but also provides a pathway for drivers to potentially reduce their insurance claims, thus creating a mutually beneficial scenario for both insurance companies and consumers alike. Conversely, the issue of congestion control emerges as a critical challenge when the volume of vehicles on the road reaches a point  
Figure 4: An illustration of the multilayered, unsupervised, learning-based, and VANET-enabled ITS framework.  
of significant density; in such scenarios, the high data rate requirements associated with effective communication necessitate the exploration of innovative and creative approaches to effectively manage and mitigate these issues. Consequently, conducting research that is centered on the potential utilization of higher-frequency ranges, specifically those that encompass millimeter wave (mmWave) and sub-terahertz (sub-THz) frequencies, could prove to be immensely beneficial as it may significantly enhance the capability to address and overcome the aforementioned challenges associated with congestion and communication in a densely populated vehicular environment.  
Conceptualization, A.N., A.I. and T.K.; methodology, A.N. and A.I.; validation, A.N., A.I. and S.W.K.; writing--original draft preparation, A.N., A.I. and T.K.; writing--review and editing, A.N., A.I. and S.W.K.; funding acquisition, S.W.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1A6A1A03039493) and in part by the NRF grant funded by the Korea government (MSIT) (NRF-2022R1A2C1004401).  
Not applicable.  
Data availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.