An Intelligent Data Driven Model To Secure Intravechile Communication Based On Machine Learning

Abstract

Traffic congestion and road accidents are major challenges in intelligent transportation systems. This project presents a Vehicle-to-Vehicle (V2V) Communication System using Machine Learning to support traffic congestion analysis and accident severity prediction. The proposed system processes traffic, weather, and accident-related data through data preprocessing and machine learning techniques. Support Vector Machine (SVM), Decision Tree, and Random Forest algorithms are considered for prediction tasks. The system aims to provide useful traffic information, accident alerts, and route recommendations to improve road safety and traffic management. V2V communication supports the exchange of relevant information between vehicles, while security considerations include authentication, encryption, and threat detection. The proposed approach demonstrates how machine learning and vehicle communication technologies can contribute to intelligent transportation systems. The effectiveness of the system should be evaluated using the actual experimental results obtained during implementation.