Dual-Input and Multi-Channel Convolutional Neural Network Model for Vehicle Speed Prediction

With the development of technology, speed prediction has become an important part of intelligent vehicle control strategies. However, the time-varying and nonlinear nature of vehicle speed increases the complexity and difficulty of prediction. Therefore, a CNN-based neural network architecture with...

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Autores principales:	Jiaming Xing, Liang Chu, Chong Guo, Shilin Pu, Zhuoran Hou
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	speed prediction vehicle signals CNN ECMS Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/680460ca438a4f3a9312a0eeda9921e1
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id	oai:doaj.org-article:680460ca438a4f3a9312a0eeda9921e1
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spelling	oai:doaj.org-article:680460ca438a4f3a9312a0eeda9921e12021-11-25T18:59:09ZDual-Input and Multi-Channel Convolutional Neural Network Model for Vehicle Speed Prediction10.3390/s212277671424-8220https://doaj.org/article/680460ca438a4f3a9312a0eeda9921e12021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7767https://doaj.org/toc/1424-8220With the development of technology, speed prediction has become an important part of intelligent vehicle control strategies. However, the time-varying and nonlinear nature of vehicle speed increases the complexity and difficulty of prediction. Therefore, a CNN-based neural network architecture with two channel input (DICNN) is proposed in this paper. With two inputs and four channels, DICNN can predict the speed changes in the next 5 s by extracting the temporal information of 10 vehicle signals and the driver’s intention. The prediction performances of DICNN are firstly examined. The best RMSE, MAE, ME and R<sup>2</sup> are obtained compared with a Markov chain combined with Monte Carlo (MCMC) simulation, a support vector machine (SVM) and a single input CNN (SICNN). Secondly, equivalent fuel consumption minimization strategies (ECMS) combining different vehicle speed prediction methods are constructed. After verification by simulation, the equivalent fuel consumption of the simulation increases by only 4.89% compared with dynamic-programming-based energy management strategy and decreased by 5.40% compared with the speed prediction method with low accuracy.Jiaming XingLiang ChuChong GuoShilin PuZhuoran HouMDPI AGarticlespeed predictionvehicle signalsCNNECMSChemical technologyTP1-1185ENSensors, Vol 21, Iss 7767, p 7767 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	speed prediction vehicle signals CNN ECMS Chemical technology TP1-1185
spellingShingle	speed prediction vehicle signals CNN ECMS Chemical technology TP1-1185 Jiaming Xing Liang Chu Chong Guo Shilin Pu Zhuoran Hou Dual-Input and Multi-Channel Convolutional Neural Network Model for Vehicle Speed Prediction
description	With the development of technology, speed prediction has become an important part of intelligent vehicle control strategies. However, the time-varying and nonlinear nature of vehicle speed increases the complexity and difficulty of prediction. Therefore, a CNN-based neural network architecture with two channel input (DICNN) is proposed in this paper. With two inputs and four channels, DICNN can predict the speed changes in the next 5 s by extracting the temporal information of 10 vehicle signals and the driver’s intention. The prediction performances of DICNN are firstly examined. The best RMSE, MAE, ME and R<sup>2</sup> are obtained compared with a Markov chain combined with Monte Carlo (MCMC) simulation, a support vector machine (SVM) and a single input CNN (SICNN). Secondly, equivalent fuel consumption minimization strategies (ECMS) combining different vehicle speed prediction methods are constructed. After verification by simulation, the equivalent fuel consumption of the simulation increases by only 4.89% compared with dynamic-programming-based energy management strategy and decreased by 5.40% compared with the speed prediction method with low accuracy.
format	article
author	Jiaming Xing Liang Chu Chong Guo Shilin Pu Zhuoran Hou
author_facet	Jiaming Xing Liang Chu Chong Guo Shilin Pu Zhuoran Hou
author_sort	Jiaming Xing
title	Dual-Input and Multi-Channel Convolutional Neural Network Model for Vehicle Speed Prediction
title_short	Dual-Input and Multi-Channel Convolutional Neural Network Model for Vehicle Speed Prediction
title_full	Dual-Input and Multi-Channel Convolutional Neural Network Model for Vehicle Speed Prediction
title_fullStr	Dual-Input and Multi-Channel Convolutional Neural Network Model for Vehicle Speed Prediction
title_full_unstemmed	Dual-Input and Multi-Channel Convolutional Neural Network Model for Vehicle Speed Prediction
title_sort	dual-input and multi-channel convolutional neural network model for vehicle speed prediction
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/680460ca438a4f3a9312a0eeda9921e1
work_keys_str_mv	AT jiamingxing dualinputandmultichannelconvolutionalneuralnetworkmodelforvehiclespeedprediction AT liangchu dualinputandmultichannelconvolutionalneuralnetworkmodelforvehiclespeedprediction AT chongguo dualinputandmultichannelconvolutionalneuralnetworkmodelforvehiclespeedprediction AT shilinpu dualinputandmultichannelconvolutionalneuralnetworkmodelforvehiclespeedprediction AT zhuoranhou dualinputandmultichannelconvolutionalneuralnetworkmodelforvehiclespeedprediction
_version_	1718410465905737728

Dual-Input and Multi-Channel Convolutional Neural Network Model for Vehicle Speed Prediction

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