Stochastic speed prediction for connected vehicles using improved bayesian networks with back propagation

查看全文 作　　者：WANG [1]LiHua;CUI [1]YaHui;ZHANG [1]FengQi;COSKUN [2]Serdar;LIU [3]KaiLong;LI [1]GuangLei

机构地区：[1]School of Mechanical and Precision Instrument Engineering,Xi'an University of Technology,Xi'an 710048,China;[2]Department of Mechanical Engineering,Tarsus University,Tarsus,Mersin 33400,Turkey;[3]WMG,University of Warwick,Coventry,CV47AL,UK

出　　处：《Science China(Technological Sciences)》索引2022年第65卷第7期,共13页

基　　金：supported by the National Natural Science Foundation of China (Grant Nos. 51905419 and 51175419)。 摘　　要：Advanced vehicular control technologies rely on accurate speed prediction to make ecological and safe decisions. This paper proposes a novel stochastic speed prediction method for connected vehicles by incorporating a Bayesian network(BN) and a Back Propagation(BP) neural network. A BN model is first designed for predicting the stochastic vehicular speed in a priori. To improve the accuracy of the BN-based speed prediction, a BP-based predicted speed error compensation module is constructed by formulating a mapping between the predicted speed and its corresponding prediction error. In the end, a filtering algorithm is developed to smoothen the compensated stochastic vehicular speed. To validate the workings of the proposed approaches in experiments, two typical scenarios are considered: one predecessor vehicle in a double-vehicle scenario and two predecessor vehicles in a multi-vehicle scenario. Simulation results under the considered scenarios demonstrate that the proposed BN-BP fusion method outperforms the BN-based method with respect to the root mean square error, standardized residuals, and R-squared, and the online prediction time of proposed fusion prediction can satisfy a real-time application requirement. The main highlighted contributions of this article are threefold:(1) We put forward an improved BN method, which is combined with a BP neural network, to construct a stochastic vehicular speed prediction method under connected driving;(2) different from existing methods, a unique interconnected framework that consists of a stochastic vehicular speed prediction module, a compensation module, and a speed smoothing module is proposed;(3) extensive simulation studies based on a set of evaluation metrics are illustrated to reveal the advantages and merits of the proposed approaches. 关键词：connected vehicles stochastic vehicular speed prediction Bayesian network BACK-PROPAGATION

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Stochastic speed prediction for connected vehicles using improved bayesian networks with back propagation

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