Empirical Study and Signal Intensity Prediction for Cellular Vehicle-to-Everything (C-V2X)

The development of autonomous driving has led to the proposal of vehicle-to-everything (V2X) to improve the reliability of autonomous driving systems through information sharing among vehicles and infrastructure. However, the high-speed mobility of autonomous vehicles and the dynamic surrounding environment make the V2X network unstable and unreliable. To address this issue, empirically studying the characteristics and predicting the signal intensity of the V2X network is crucial, which can provide more information for further optimizing communication strategies and enhancing driving safety. In this paper, we collect the real-world vehicle-to-infrastructure (V2I) performance under different driving scenarios and build the quantitative relationship between several environmental factors and the V2I performance. We also develop deep learning models to predict the V2I signal intensity based on external environmental conditions. Experimental results in real-world data demonstrate the effectiveness of our model on classification and regression tasks compared to other models. Our study aims to enhance the applications of V2X on autonomous driving and improve driving safety and traffic efficiency.