CNN and KPCA-Based Automated Feature Extraction for Real Time Driving Pattern Recognition

IEEE Access

Liang Xie¹  Huiyu Zhou²  Jili Tao³  Qianni Zhang⁴ 

[1] College of Control Science and Engineering, Zhejiang University, Hangzhou, China;Informatics, University of Leicester, Leicester, U.K.;Ningbo Institute of Technology, Zhejiang University, Ningbo, China;School of Electronic Engineering and Computer Science, Queen Mary University of London, London, U.K.;

关键词: Convolution neural network;    driving pattern recognition;    feature selection;    kernel principle component analysis;   

DOI  :  10.1109/ACCESS.2019.2938768

来源: DOAJ

【 摘 要 】

Driving conditions greatly affect the energy control and the fuel economy of a hybrid electric vehicle (HEV). In this paper, an automated feature extraction scheme based on convolution neural networks (CNNs) and Kernel PCA (KPCA) for real time driving pattern recognition (RTDPR) is proposed in order to achieve consistent performance of the energy management. Firstly, a dimension expanding strategy is performed to transform one-dimensional speed sequences to generate a two-dimensional dataset. Then, the transformed data is sent to the CNN and KPCA based feature extractor. Finally, the feature extractor automatically selects the most representative features for classification. To improve the generalization of CNN to a small sample dataset, the structure of the typical CNN is adjusted by adding the KPCA layer in order to reduce model parameters. The model is well trained and evaluated in simulation, and it is tested for RTDPR in the real world. Simulation and experimental results show that the proposed automated feature extraction strategy outperforms the conventional driving pattern recognition algorithms based on manually feature extraction, which has achieved the state-of-the-art recognition accuracy.

【 授权许可】

Unknown