MTS-DeepNet for lane change prediction
Xipeng Wang, Yi Lu Murphey, Dev S. Kochhar · 2016
Time series data are ubiquitous and are of importance in many application problems in engineering, science, medicine, economics and entertainment. Many real world pattern classification problems involve the processing and analysis of multiple variables in the temporal domain. These types of problems are referred to as Multivariate Time Series (MTS) problems. In many real-world applications, an MTS problem can involve a large number of signals, and require algorithms to select signals and extract temporal and spatial features from them. In this paper, we present an innovative convolutional neural network, MTS-DeepNet that is specially designed for MTS pattern classification. The system integrates signal and feature selections with MTS pattern classification in one learning framework. MTS-DeepNet is applied to a real-world problem, namely predicting driver lane departure based on driver's physiological signals. Our experimental results showed that, in comparison to a multi-layer neural network trained with the backpropagation algorithm, MTS-DeepNet gave better prediction accuracy.