Data driven vehicular heterogeneity based intelligent collision avoidance system for Internet of Vehicles (IoV)

The Internet of Vehicles (IoV) is an emerging technology that aims to connect vehicles, infrastructure, and other devices to enable intelligent transportation systems. One of the key challenges in IoV is to ensure safe and efficient communication among vehicles of different types and capabilities. This paper proposes a data-driven vehicular heterogeneity-based intelligent collision avoidance system for IoV. The system leverages Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication to collect real-time data about the environment and the vehicles. The data is collected to acknowledge the heterogeneity of vehicles and human behavior. The data is analyzed using machine learning algorithms to identify potential collision risks and recommend appropriate actions to avoid collisions. The system takes into account the heterogeneity of vehicles, such as their size, speed, and maneuverability, to optimize collision avoidance strategies. The proposed system is experimented with real-time datasets and compared with existing collision avoidance systems. The results are shown using the evaluation metrics that show the proposed system can significantly reduce the number of collisions and improve the overall safety and efficiency of IoV with an accuracy of 96.5% using the SVM algorithm. The trial outcomes demonstrated that the new system, incorporating vehicular, weather, and human behavior factors, outperformed previous systems that only considered vehicular and weather aspects. This innovative approach is poised to lead transportation efforts, reducing accident rates and improving the quality of transportation systems in smart cities. By offering predictive capabilities, the proposed model not only helps control accident rates but also prevents them in advance, ensuring road safety.