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Neural Computing and Applications

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09-08-2018 | S.I. : Machine Learning Applications for Self-Organized Wireless Networks

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

Authors: Xuan Zhao, Shu Wang, Jian Ma, Qiang Yu, Qiang Gao, Man Yu

Published in: Neural Computing and Applications | Special Issue 1/2019

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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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Title: Identification of driver’s braking intention based on a hybrid model of GHMM and GGAP-RBFNN
Authors: Xuan Zhao
Shu Wang
Jian Ma
Qiang Yu
Qiang Gao
Man Yu
Publication date: 09-08-2018
Publisher: Springer London
Published in: Neural Computing and Applications / Issue Special Issue 1/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-3672-1

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