Integrated Velocity Prediction Method and Application in Vehicle-Environment Cooperative Control Based on Internet of Vehicles

Rapid progress has been gained in the field of advanced communication technologies, which also promote parallel developments in the Internet of Vehicles (IoVs). In this context, vehicle-environment cooperative control can be integrated into next-generation vehicles to further improve the vehicle's performance, in particular energy efficiency. Accurate prediction of future velocity profiles on basis of IoVs can be a critical breakthrough, which can contribute much to vehicle operation efficiency promotion. In this paper, an integrated velocity prediction (IVP) method fully taking advantage of IoVs is proposed and demonstrated through a case study. In the IVP method, both the macroscopically and microscopically predicted velocity profiles are considered. The macroscopic velocity profiles are predicted via traffic flow analysis (TFA) in multi-access edge computing units (MECUs) which are situated alongside the route. Microscopic velocity profiles are forecasted through Mondrian forest (MF) algorithm in the on-board vehicle control unit (VCU). Final velocity prediction is generated through combination of the macroscopic and microscopic profiles in frequency domain in on-board VCU through fast Fourier transform (FFT) and inverse FFT. A case study validates the distinguished performance of IVP method and demonstrates its significant contribution to vehicle performance improvement.