Stochastic Velocity Prediction for Connected Vehicles Considering V2V Communication Interruption

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dc.authoridCoskun, Serdar/0000-0002-7080-0340
dc.authoridZhang, Fengqi/0000-0001-9811-2593
dc.contributor.authorWang, Lihua
dc.contributor.authorZhang, Fengqi
dc.contributor.authorCui, Yahui
dc.contributor.authorCoskun, Serdar
dc.contributor.authorTang, Xiaolin
dc.contributor.authorYang, Yalian
dc.contributor.authorHu, Xiaosong
dc.date.accessioned2025-03-17T12:25:38Z
dc.date.available2025-03-17T12:25:38Z
dc.date.issued2023
dc.departmentTarsus Üniversitesi
dc.description.abstractReliable and accurate velocity prediction can significantly contribute to the quality of connected vehicle control applications. Existing efforts focus on the velocity prediction without considering vehicle-to-vehicle (V2V) communication interruption. Hence, a stochastic velocity prediction method for connected vehicles considering V2V communication interruption is put forward for the first time. The missing V2V communication data are addressed by the piecewise cubic Hermite spline interpolation. Then, the processed data are used as the input variables of the best conditional linear Gaussian (CLG) prediction model. Specifically, the best CLG model is obtained by analyzing the influence of different input variables on the velocity prediction without V2V communication interruption. The results demonstrate that the prediction accuracy of CLG-based model is acceptable if the communication interruption time is less than 5 s compared to the non-interrupted V2V communication case. The sensitivity study of the best CLG model under multiple vehicles scenario indicates that choosing appropriate historical data substantially improve the prediction accuracy. Furthermore, the CLG-based predictor is proved to be an effective method to achieve higher prediction accuracy in two test road networks when compared with the Back-propagation and Long Short-Term Memory network.
dc.description.sponsorshipNational Natural Science Foundation of China [51905419]
dc.description.sponsorship& nbsp;This work was supported in part by the National Natural Science Foundation of China under Grant 51905419.
dc.identifier.doi10.1109/TITS.2023.3293116
dc.identifier.endpage11667
dc.identifier.issn1524-9050
dc.identifier.issn1558-0016
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85165295390
dc.identifier.scopusqualityQ1
dc.identifier.startpage11654
dc.identifier.urihttps://doi.org/10.1109/TITS.2023.3293116
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1791
dc.identifier.volume24
dc.identifier.wosWOS:001035831600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Transactions On Intelligent Transportation Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250316
dc.subjectStochastic velocity prediction
dc.subjectconnected vehicles
dc.subjectV2V communication interruption
dc.subjectpiecewise cubic Hermite spline interpolation
dc.titleStochastic Velocity Prediction for Connected Vehicles Considering V2V Communication Interruption
dc.typeArticle

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