AI-Assisted Dynamic RSU Deployment Using Real-Time Traffic Density Estimation in VANETs

Abstract

With the rapid advancement of vehicular communication technologies, maintaining reliable connectivity in Vehicular Ad Hoc Networks (VANETs) has become a critical challenge due to high mobility, dynamic topology, and uneven traffic distribution. Frequent disconnections in Vehicle-to-Vehicle (V2V) communication lead to increased latency and reduced network performance. To address these issues, this research proposes an AI-assisted dynamic Roadside Unit (RSU) deployment framework that leverages real-time traffic density estimation to optimize communication infrastructure. The proposed system utilizes deep learning-based vehicle detection models to analyze real-time traffic images and estimate vehicle density across different road segments. The extracted traffic information is further processed using machine learning techniques to predict communication demand and identify potential connectivity gaps. Based on these predictions, the system dynamically activates, deactivates, or repositions RSUs to ensure continuous network coverage and reduce dependency on unstable V2V links. The optimization model focuses on minimizing communication delay, enhancing packet delivery ratio, and improving overall network reliability through adaptive RSU placement. Additionally, a hybrid communication approach combining V2V and Vehicle-to-Infrastructure (V2I) is employed to overcome connectivity loss in sparse or highly dynamic traffic conditions. Simulation results demonstrate that the proposed AI-driven framework significantly improves network throughput, reduces communication latency, and ensures stable connectivity compared to traditional static RSU deployment strategies. The system effectively adapts to varying traffic patterns, making it suitable for next-generation intelligent transportation systems and smart city applications.