Abstract
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.
Highlights
EMERGING development of Internet of Vehicles (IoVs) is providing a vast of opportunities for the development of novel solutions which can be implemented to improve vehicle’s operation performance [1, 2]
The macroscopic velocity profiles are predicted via traffic flow analysis (TFA) in multi-access edge computing units (MECUs) which are situated alongside the route
It is noteworthy that the promising performance is attained on the assumption that no disturbance on macroscopic velocity prediction emerges from multi-rate situation of global navigation satellite system (GNSS)
Summary
EMERGING development of Internet of Vehicles (IoVs) is providing a vast of opportunities for the development of novel solutions which can be implemented to improve vehicle’s operation performance [1, 2]. The superior communication ability which can be achieved through IoVs has accelerated the progress in vehicle-environment cooperative control solutions, such as multi-access edge computing (MEC) [3, 4]. As the accuracy of velocity prediction will inevitably influence the effectiveness and viability of vehicle-environment cooperative control, there is significant effort being made to develop reliable precise forecasting methods. The prediction accuracy needs to be balanced against the computational intensity and algorithmic complexity, in order that the prediction algorithm can be implemented in real time
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