Identification of driver’s braking intention based on a hybrid model of GHMM and GGAP-RBFNN
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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.
KeywordsBraking intention identification Gaussian mixture-hidden Markov model (GHMM) Generalized growing and pruning radial basis function neural network (GGAP-RBFNN) Advanced driver assistance system (ADAS)
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).
- 16.Imamura T, Ogi T, Zhang Z, et al (2013) Study of induction and estimation method for driver’s intention by using a driving simulator. In: IEEE international conference on systems, man, and cybernetics, pp 3322–3326. https://doi.org/10.1109/smc.2013.566
- 18.Pentland A, Liu A (1999) Modeling and prediction of human behavior. MIT Press, CambridgeGoogle Scholar
- 22.Amsalu SB, Homaifar A, Karimoddini A, et al (2017) Driver intention estimation via discrete hidden Markov model. In: IEEE international conference on systems, man, and cybernetics, pp 2712–2717. https://doi.org/10.1109/smc.2017.8123036
- 23.Amsalu SB, Homaifar A, Afghah F, et al (2015) Driver behavior modeling near intersections using support vector machines based on statistical feature extraction. In: IEEE intelligent vehicles symposium, pp 1270–1275. https://doi.org/10.1109/ivs.2015.7225857
- 24.Rodemerk C, Winner H, Kastner R (2015) Predicting the driver’s turn intentions at urban intersections using context-based indicators. In: IEEE intelligent vehicles symposium, pp 964–969. https://doi.org/10.1109/ivs.2015.7225809
- 26.Kim JW, Kim IH, Lee SW (2016) Detection of braking intention during simulated driving based on EEG analysis: online study. In: IEEE international conference on systems, man, and cybernetics, pp 887–891. https://doi.org/10.1109/smc.2015.163
- 29.Wang H, Bi L, Teng T (2017) EEG-based emergency braking intention prediction for brain-controlled driving considering one electrode falling-off. In: Engineering in medicine and biology society, pp 2494–2497. https://doi.org/10.1109/embc.2017.8037363
- 44.Zhang Y, Yu Z, Xu L et al (2009) A study on the strategy of braking force distribution for the hybrid braking system in electric vehicles based on braking intention. Automot Eng 31(3):244–249. https://doi.org/10.3321/j.issn:1000-680X.2009.03.011 Google Scholar
- 45.Wang Y, Ning G, Yu Z (2011) Parameter selection for the identification of driver’s braking intention for passenger car. Automot Eng 33(3):213–216. https://doi.org/10.19562/j.chinasae.qcgc.2011.03.007 Google Scholar