Vehicle speed prediction using a convolutional neural network combined with a gated recurrent unit with attention

Dongxue Zhang, Zhennan Wang, X. Jiao, Zhao Zhang
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引用次数: 0

Abstract

Vehicle speed prediction can facilitate many applications, such as optimizing vehicle propulsion systems and designing advanced driver assistance control systems. In a complex and variable traffic environment, many dynamic factors affect vehicle speed and make it difficult to predict accurately. The development of intelligent transportation systems and machine learning methods makes it possible to predict short-term vehicle speed accurately. A novel vehicle speed prediction model is proposed in this paper to improve prediction accuracy based on a deep learning method. A practical temporal and channel attention module (TCAM) is designed for convolutional neural networks (CNNs) to strengthen meaningful information and reduce the amount of unnecessary information. A gated recurrent unit (GRU) network with an attention mechanism is constructed to explore significant hidden relationships among time-series data with its memory function. These two subprediction models are fused to enhance the performance of vehicle speed prediction. Simulation experiments using IPG Carmaker software validate that the proposed model provides better predictive accuracy than traditional and existing vehicle speed prediction methods based on deep learning.
使用卷积神经网络结合门控递归单元预测车速
车速预测可以促进许多应用,例如优化车辆推进系统和设计先进的驾驶员辅助控制系统。在复杂多变的交通环境中,许多动态因素都会影响车速,因此很难准确预测。智能交通系统和机器学习方法的发展使得准确预测短期车速成为可能。本文提出了一种基于深度学习方法的新型车辆速度预测模型,以提高预测精度。本文为卷积神经网络(CNN)设计了一个实用的时间和通道注意模块(TCAM),以加强有意义的信息并减少不必要的信息量。构建了一个具有注意机制的门控递归单元(GRU)网络,利用其记忆功能探索时间序列数据之间的重要隐藏关系。这两个子预测模型被融合在一起,以提高车辆速度预测的性能。使用 IPG Carmaker 软件进行的仿真实验验证了所提出的模型比传统的和现有的基于深度学习的车速预测方法具有更好的预测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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