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CLC number: TP391; U463.6

On-line Access: 2012-03-01

Received: 2011-07-17

Revision Accepted: 2011-12-07

Crosschecked: 2012-02-09

Cited: 8

Clicked: 3711

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Journal of Zhejiang University SCIENCE C 2012 Vol.13 No.3 P.208-217


Driving intention recognition and behaviour prediction based on a double-layer hidden Markov model

Author(s):  Lei He, Chang-fu Zong, Chang Wang

Affiliation(s):  State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China

Corresponding email(s):   jlu_helei@126.com, cfzong@yahoo.com.cn

Key Words:  Vehicle engineering, Driving intention recognition, Driving behaviour prediction, Driver model, Double-layer hidden Markov model (HMM)

Lei He, Chang-fu Zong, Chang Wang. Driving intention recognition and behaviour prediction based on a double-layer hidden Markov model[J]. Journal of Zhejiang University Science C, 2012, 13(3): 208-217.

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%T Driving intention recognition and behaviour prediction based on a double-layer hidden Markov model
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%A Chang-fu Zong
%A Chang Wang
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T1 - Driving intention recognition and behaviour prediction based on a double-layer hidden Markov model
A1 - Lei He
A1 - Chang-fu Zong
A1 - Chang Wang
J0 - Journal of Zhejiang University Science C
VL - 13
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SP - 208
EP - 217
%@ 1869-1951
Y1 - 2012
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C11a0195

We propose a model structure with a double-layer hidden Markov model (HMM) to recognise driving intention and predict driving behaviour. The upper-layer multi-dimensional discrete HMM (MDHMM) in the double-layer HMM represents driving intention in a combined working case, constructed according to the driving behaviours in certain single working cases in the lower-layer multi-dimensional Gaussian HMM (MGHMM). The driving behaviours are recognised by manoeuvring the signals of the driver and vehicle state information, and the recognised results are sent to the upper-layer HMM to recognise driving intentions. Also, driving behaviours in the near future are predicted using the likelihood-maximum method. A real-time driving simulator test on the combined working cases showed that the double-layer HMM can recognise driving intention and predict driving behaviour accurately and efficiently. As a result, the model provides the basis for pre-warning and intervention of danger and improving comfort performance.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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Open peer comments: Debate/Discuss/Question/Opinion


Teng Fei@Beijing Institute of tech<19283746_2008@sohu.com>

2014-03-31 16:20:25

I wonna know more about Markov model

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