Mechanisms for the Estimation of Prediction Intervals in Vehicular Communication Scenarios

Advanced vehicular applications are foreseen to rely on wireless connectivity. By predicting wireless network performance, application adaptations can be done a priori to ensure robust and efficient operations. However, determining accurate predictions of network performance or factors affecting it is challenging in highly dynamic vehicular environments. In this paper, we address the problem of determining prediction intervals for future observations. Specifically, prediction intervals are determined for wireless link quality between two vehicles that communicate directly with each other. Evaluations based on real-world vehicle-to-vehicle dataset show that the proposed mechanism is able to determine prediction intervals with the desired confidence level in dynamic scenarios. Furthermore, the mechanism is capable of identifying the intervals that are unreliable with respect to coverage requirements.