### Organization  
The structure of this paper is as follows: The system model is described in detail in Section 2, including its components and architecture. The methodology and approach used to tackle the challenges faced in VANET communication are outlined in Section 3 as a proposed solution. In Section 4, simulation results are provided, showing how effectively the solution fares in different scenarios in terms of performance. Finally, Section 5 gives an overall conclusion that summarizes the main findings and contributions of the research work.  
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Reference** & **Study** & **Key Contributions** & **Literature Gap** \\ \hline
[1] & [PERSON] et al. (2022) & Highlights road traffic accidents as a leading cause of death among young adults and children. Discusses the economic burden on low and middle-income countries. & The study does not propose specific technological solutions to address these fatalities and lacks integration with advanced communication systems. \\ \hline
[2] & [PERSON] et al. (2024) & Emphasizes the economic impact of road traffic accidents on national GDP (about 3\%). & No focus on innovative approaches to mitigate fatalities using IoT or AI technologies. \\ \hline
[3] & [PERSON] et al. (2000) & Discusses the UN goal of reducing road traffic deaths by 50\% by 2030. & Does not elaborate on how technological advancements in communication or AI can contribute to this goal. \\ \hline
[5; 6; 7] & [PERSON] et al. (2015); & Introduces the integration of wireless communication and sensors for data acquisition, with a focus on long-distance communication. & Limited exploration of how these technologies can be applied to VANET and ITS for road safety. \\ \hline
[10, 11] & [PERSON] et al. (2017); & Discusses the role of 5G, mMTC, and URLLC in enhancing IoT device connectivity and reliability. & Limited focus on vehicle communication networks and safety improvements via 5G advancements. \\ \hline
[14] & [PERSON] et al. (2022) & Reviews V2X communication, especially New Radio V2X (NR-V2X) and Sidelink communication, allowing direct vehicle-to-device communication. & Insufficient discussion of AI applications for optimizing V2X communication and scalability in VANETs. \\ \hline
[16] & [PERSON] et al. (2023) & Details advances in VANET communication, addressing safety, commercial, and infotainment services. & Does not propose AI-based frameworks to further enhance VANET communication reliability and efficiency. \\ \hline
[17; 18] & [PERSON] et al. (2020); & Discusses VANETs as a type of MANET, highlighting their importance in Intelligent Transportation Systems (ITSs). & Lacks a structured AI approach to optimize communication in VANETs. \\ \hline
[13; 20; 28] & [PERSON] et al. (2021, 2024); & Recognizes AI’s potential (ML, DL, and SI) in solving VANET challenges, with K-means clustering showing promise for data exchange in VANETs. \\ \hline N/A & This paper (2024) & Proposes a multi-layered VANET-enabled ITS framework using K-means clustering to improve scalability and reliability, contributing towards the UN goal of reducing road fatalities by 50\%. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of existing literature on road traffic safety, VANETs, and AI applications with identified research gaps.