This paper presents the development of an algorithm for Vehicle-to-Vehicle (V2V) communication, a crucial technology in Intelligent Transportation Systems (ITS) that holds significant potential for enhancing road safety and traffic efficiency. One of the most common types of vehicle collisions occurs at intersections, particularly those without traffic lights. This study focuses on creating a V2V algorithm designed to prevent collisions in such scenarios. The findings were presented through visual simulations that depict various scenarios involving vehicles approaching an intersection. The algorithm follows a two-step process: Firstly, it utilizes Dedicated Short-Range Communication Systems (DSRCS) to accurately estimate the distance between vehicles. Leveraging this distance information, the algorithm dynamically adjusts the speed of each vehicle. The algorithm's performance is assessed using Convolutional Neural Networks (CNN), which enables a comprehensive evaluation of its reliability and efficiency in V2V communication. The algorithm demonstrates notable enhancements in the reliability and efficiency of V2V communication. This paper serves as a validation of the feasibility of developing more advanced V2V communication algorithm and potentially making significant contributions to the advancement of ITS.
