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Article Navigation> JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY> 2018> 27(3): 357-362
Min Li, Wuhong Wang, Xiaobei Jiang, Tingting Gao, Qian Cheng. Identification of Driving Intention Based on EEG Signals[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2018, 27(3): 357-362. doi: 10.15918/j.jbit1004-0579.17176
Citation: Min Li, Wuhong Wang, Xiaobei Jiang, Tingting Gao, Qian Cheng. Identification of Driving Intention Based on EEG Signals[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2018, 27(3): 357-362.doi:10.15918/j.jbit1004-0579.17176
shu

Identification of Driving Intention Based on EEG Signals

doi:10.15918/j.jbit1004-0579.17176
  • 1.

    School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China;School of Automobile & Transportation, Qingdao University of Technology, Qingdao 266520, Shandong, China

  • 2.

    School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

  • Received Date:2017-11-26
    Abstract
  • The driver's intention is recognized by electroencephalogram(EEG) signals under different driving conditions to provide theoretical and practical support for the applications of automated driving. An EEG signal acquisition system is established by designing a driving simulation experiment, in which data of the driver's EEG signals before turning left, turning right, and going straight, are collected in a specified time window. The collected EEG signals are analyzed and processed by wavelet packet transform to extract characteristic parameters. A driving intention recognition model, based on neural network, is established, and particle swarm optimization (PSO) is adopted to optimize the model parameters. The extracted characteristic parameters are inputted into the recognition model to identify driving intention before turning left, turning right, and going straight. Matlab is used to simulate and verify the established model to obtain the results of the model.The maximum recognition rate of driving intention is 92.9%. Results show that the driver's EEG signal can be used to analyze the law of EEG signals. Furthermore, the PSO-based neural network model can be adapted to recognize driving intention.
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    • References (11)
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