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Identification of driver’s braking intention based on a hybrid model of GHMM and GGAP-RBFNN

  • S.I. : Machine Learning Applications for Self-Organized Wireless Networks
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Abstract

Driving intention has been widely used in intelligent driver assistance systems, automated driving systems, and electric vehicle control strategies. The accuracy, practicality, and timeliness of the driving intention identification model are its key issues. In this paper, a novel driver’s braking intention identification model based on the Gaussian mixture-hidden Markov model (GHMM) and generalized growing and pruning radial basis function neural network (GGAP-RBFNN) is proposed to improve the identification accuracy of the model. The simplest brake pedal and vehicle speed data that are easily obtained from the vehicle are used as an observation sequence to improve practicality of the model. The data of the pressing brake pedal stage are used to identify the braking intention to improve the timeliness of the model. The experimental data collected from real vehicle tests are used for off-line training and online identification. The research results show that the accuracy of driver’s braking intention identification model based on the GHMM/GGAP-RBFNN hybrid model is 94.69% for normal braking and 95.57% for slight braking, which are, respectively, 26.55% and 17.72% higher than achieved by the GHMM. In addition, the data of the pressing brake pedal stage are used for intention identification, which is 1.2 s faster than that of the existing identification model based on the GHMM.

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Acknowledgements

This research is funded by the National Key R&D Program of China (2017YFC0803904), National Natural Science Foundation of China (51507013), China Postdoctoral Science Foundation (2018T111006, 2017M613034), Postdoctoral Science Foundation of Shaanxi Province (2017BSHEDZZ36), Shaanxi Province Industrial Innovation Chain Project (2018ZDCXL-GY-05-03-01), Shaanxi Province Key Research and Development Plan Project (2018ZDXM-GY-082), Shaanxi Innovative Talents Promotion Plan Project (2018KJXX-005).

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Authors and Affiliations

  1. School of Automobile, Chang’an University, Xi’an, People’s Republic of China

    Xuan Zhao, Shu Wang, Jian Ma, Qiang Yu & Man Yu

  2. Department of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, People’s Republic of China

    Qiang Gao

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  1. Xuan Zhao
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  2. Shu Wang
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Correspondence to Xuan Zhao.

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Zhao, X., Wang, S., Ma, J. et al. Identification of driver’s braking intention based on a hybrid model of GHMM and GGAP-RBFNN. Neural Comput & Applic 31 (Suppl 1), 161–174 (2019). https://doi.org/10.1007/s00521-018-3672-1

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