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Neural Computing and Applications

, Volume 31, Supplement 1, pp 161–174 | Cite as

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

  • Xuan ZhaoEmail author
  • Shu Wang
  • Jian Ma
  • Qiang Yu
  • Qiang Gao
  • Man Yu
S.I. : Machine Learning Applications for Self-Organized Wireless Networks
  • 151 Downloads

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.

Keywords

Braking intention identification Gaussian mixture-hidden Markov model (GHMM) Generalized growing and pruning radial basis function neural network (GGAP-RBFNN) Advanced driver assistance system (ADAS) 

Notes

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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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  • Xuan Zhao
    • 1
    Email author
  • Shu Wang
    • 1
  • Jian Ma
    • 1
  • Qiang Yu
    • 1
  • Qiang Gao
    • 2
  • Man Yu
    • 1
  1. 1.School of AutomobileChang’an UniversityXi’anPeople’s Republic of China
  2. 2.Department of Mechanical EngineeringNanjing University of Science and TechnologyNanjingPeople’s Republic of China

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