Multivariate time series prediction of lane changing behavior using deep neural network
Multivariate time series prediction of lane changing behavior using deep neural network
复制标题
DOI：
10.1007/s10489-018-1163-9
复制
发表时间：
2018-10-01
影响因子：
5.3
通讯作者：
Zhu, Honghui
中科院分区：
文献类型：
--
作者：
Gao, Jun;Murphey, Yi Lu;Zhu, Honghui
许多真实的模式分类问题涉及时间域中多个变量的处理和分析。这种类型的问题被称为多变量时间序列（MTS）问题。由于时间序列数据的高维性、大数据量和不断更新的特点，它仍然是一个具有挑战性的问题。在本文中，我们使用三种类型的生理信号，从驾驶员预测车道变化之前的事件实际发生。这些是心电图（ECG），皮肤电反应（GSR）和呼吸率（RR），并在先前的研究中确定，以最好地反映驾驶员对驾驶环境的反应。提出了一种新的分组卷积神经网络（MTS-GCNN）模型用于MTS模式分类。在我们的MTS-GCNN模型中，我们在训练阶段提出了一种新的结构学习算法。该算法利用多个时间序列的协方差结构将输入量划分为组，然后通过谱聚类对输入序列进行聚类来显式学习MTS-GCNN结构。与其他基于特征的分类方法不同，我们的MTS-GCNN可以选择和提取合适的内部结构，通过卷积和下采样操作自动生成时间和空间特征。实验结果表明，与其他最先进的模型相比，我们的MTS-GCNN在预测精度方面表现得更好。
Many real world pattern classification problems involve the process and analysis of multiple variables in temporal domain. This type of problem is referred to as Multivariate Time Series (MTS) problem. It remains a challenging problem due to the nature of time series data: high dimensionality, large data size and updating continuously. In this paper, we use three types of physiological signals from the driver to predict lane changes before the event actually occurs. These are the electrocardiogram (ECG), galvanic skin response (GSR), and respiration rate (RR) and were determined, in prior studies, to best reflect a driver's response to the driving environment. A novel Group-wise Convolutional Neural Network, MTS-GCNN model is proposed for MTS pattern classification. In our MTS-GCNN model, we present a new structure learning algorithm in training stage. The algorithm exploits the covariance structure over multiple time series to partition input volume into groups, then learns the MTS-GCNN structure explicitly by clustering input sequences with spectral clustering. Different from other feature-based classification approaches, our MTS-GCNN can select and extract the suitable internal structure to generate temporal and spatial features automatically by using convolution and down-sample operations. The experimental results showed that, in comparison to other state-of-the-art models, our MTS-GCNN performs significantly better in terms of prediction accuracy.