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

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dc.authoridCoskun, Serdar/0000-0002-7080-0340
dc.authoridLIU, KAILONG/0000-0002-3564-6966
dc.contributor.authorWang Lihua
dc.contributor.authorCui Yahui
dc.contributor.authorZhang Fengqi
dc.contributor.authorCoskun Serdar
dc.contributor.authorLiu Kailong
dc.contributor.authorLi Guanglei
dc.date.accessioned2025-03-17T12:27:35Z
dc.date.available2025-03-17T12:27:35Z
dc.date.issued2022
dc.departmentTarsus Üniversitesi
dc.description.abstractAdvanced 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 whose 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, 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.
dc.description.sponsorshipNational Natural Science Foundation of China [51905419, 51175419]
dc.description.sponsorshipThis work was supported by the National Natural Science Foundation of China (Grant Nos. 51905419 and 51175419).
dc.identifier.doi10.1007/s11431-021-2037-8
dc.identifier.endpage1536
dc.identifier.issn1674-7321
dc.identifier.issn1869-1900
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85132111688
dc.identifier.scopusqualityQ1
dc.identifier.startpage1524
dc.identifier.urihttps://doi.org/10.1007/s11431-021-2037-8
dc.identifier.urihttps://hdl.handle.net/20.500.13099/2311
dc.identifier.volume65
dc.identifier.wosWOS:000812017500002
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherScience Press
dc.relation.ispartofScience China-Technological Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250316
dc.subjectconnected vehicles
dc.subjectstochastic vehicular speed prediction
dc.subjectBayesian network
dc.subjectback-propagation
dc.titleStochastic speed prediction for connected vehicles using improved bayesian networks with back propagation
dc.typeArticle

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