CNN and KPCA-based automated feature extraction for real time driving pattern recognition

Liang Xie, Jili Tao*, Qianni Zhang, Huiyu Zhou

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)
    2 Downloads (Pure)


    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.

    Original languageEnglish
    Pages (from-to)123765-123775
    Number of pages11
    JournalIEEE Access
    Early online date02 Sep 2019
    Publication statusPublished - 13 Sep 2019

    Bibliographical note

    Funding Information:
    This work was supported in part by the National Natural Science Foundation of China under Grant 61603337, in part by the Zhejiang Province Natural Science Fund under Grant LY19F030009, and in part by the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (ICT1900362). The work of H. Zhou was supported in part by the U.K. EPSRC under Grant EP/N011074/1, in part by the Royal Society-Newton Advanced Fellowship under Grant NA160342, and in part by the European Union’s Horizon 2020 Research and Innovation Program through the Marie-Sklodowska-Curie under Grant 720325.

    Publisher Copyright:
    © 2013 IEEE.

    Copyright 2020 Elsevier B.V., All rights reserved.


    • Convolution neural network
    • driving pattern recognition
    • feature selection
    • kernel principle component analysis

    ASJC Scopus subject areas

    • Computer Science(all)
    • Materials Science(all)
    • Engineering(all)

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