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Stochastic speed prediction for connected vehicles using improved bayesian networks with back propagation
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Wang, LiHua, Cui, YaHui, Zhang, FengQi, Coskun, Serdar, Liu, Kailong and Li, GuangLei (2022) Stochastic speed prediction for connected vehicles using improved bayesian networks with back propagation. Science China Technological Sciences, 65 . 1524-1536 . doi:10.1007/s11431-021-2037-8 ISSN 1674-7321.
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Official URL: http://dx.doi.org/10.1007/s11431-021-2037-8
Abstract
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 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.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
Journal or Publication Title: | Science China Technological Sciences | ||||||||
Publisher: | Springer Nature | ||||||||
ISSN: | 1674-7321 | ||||||||
Official Date: | July 2022 | ||||||||
Dates: |
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Volume: | 65 | ||||||||
Page Range: | 1524-1536 | ||||||||
DOI: | 10.1007/s11431-021-2037-8 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access |
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