## 2 Related Work
Using non-interactive zero-knowledge proof technology and a physical uncloning function, the protocol provides mutual authentication and key agreement without divulging sensitive information. A formal security study validates its ability to withstand hostile attacks while remaining efficient. The study presented in [26] depicted real-world vehicle communication scenarios that make use of 5G technology. Vehicles exchanged sensor and controlled data with one another, their surroundings, and their Digital Twins, offering insights on the practicalityof 5G Non-Standalone Architecture for certain communication scenarios, including existing latency and throughput limitations in real-world conditions. Furthermore, ref. [27] examined how 5G+ technology could be applied to the transportation sector, with a particular emphasis on driverless cars and intelligent transportation systems. By resolving issues and using 5G+ capabilities, SDN, and intelligent infrastructures to suit future user expectations for security, low latency, and energy efficiency, the adaptable framework that was suggested sought to improve smart mobility. The model proposed in [28] addressed spectrum and power allocation in V2X communication with unknown channels while taking into account various elements such as Doppler shift and inaccurate CSI. Bernstein Approximation-based and self-learning are two strong resource allocation methodologies that are robust approaches for maximizing cellular user equipment (CUE) capacity while assuring SINR for CUEs and reliability for vehicular user equipment (VUEs). Despite existing approaches in the literature, there is still a need for further investigation to improve the performance of autonomous vehicles (AVs) through the implementation of Lagrange optimization algorithms and deep learning techniques. This study aims to identify the essential parameters and conditions that can enhance AVs' energy efficiency and achievable data rate, ensuring a more efficient and reliable system. An analytical optimization technique, combined with a distributed deep learning model for AV networks, is proposed to enable AVs to adapt the effective required transmission distance, leading to enhanced efficiency and reliability in AV networks. The performance of the AV system is evaluated based on energy efficiency and achievable data rate. Table 1 compares the uniqueness of the proposed model to previous comparable research articles. Therefore, the proposed model is focused on enhancing energy efficiency and achievable data rate due to the specific requirements and limitations of Vehicle-to-Vehicle (V2V) communication. V2V communication entails vehicles to communicate with one another to improve road safety, traffic efficiency, and other applications. However, because V2V devices are frequently battery-powered, minimizing energy usage is critical for extending the operating lifetime of these devices without frequent battery changes or recharges. The correct combination of data rate and energy efficiency in V2V communication means that safety-essential information may be exchanged quickly while preserving energy resources, making it a critical concern in this specific application. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & **Technique Used** & **Optimization Problem** & \begin{tabular}{c} **Deep Learning** \\ **Technique** \\ \end{tabular}  & \ \begin{tabular}{c} **Metric for System** \\ **Evaluation** \\ \end{tabular}  & **Investigation Scenario** \\ \hline \multirow{6}{*}{[4]} & \multirow{6}{*}{Multihop relaying between AV2X; direct AV2X and V2V communications.} & Improving system QoS by determining the best autonomous inter-vehicle position from which to connect with or relay & \multirow{6}{*}{N/A} & \multirow{6}{*}{Packet delivery ratio. Throughput \(\bullet\)} & \multirow{6}{*}{Communication between AV2X is relayed with direct communication between V2V.}