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

Lei HE; Chang-fu ZONG; Chang WANG

doi:10.1631/jzus.C11a0195

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

计算机与自动化 | Updated：2022-12-09

- Driving intention recognition and behaviour prediction based on a double-layer hidden Markov model
- Driving intention recognition and behaviour prediction based on a double-layer hidden Markov model
- 信息与电子工程前沿（英文） 2012年13卷第3期页码：208-217
- Affiliations：
  
  State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China
- Author bio：
  
  [ "Lei HE, E-mail: jlu_helei@126.com" ]
  Chang-fu ZONG, E-mail: cfzong@yahoo.com.cn
- Funds：
  
  Project (Nos. 50775096 and 51075176) supported by the National Natural Science Foundation of China
- DOI：10.1631/jzus.C11a0195
  中图分类号： TP391;U463.6
- 收稿：2011-07-17，
  
  修回：2012-;2-;9，
  
  纸质出版：2012-03
- Accepted：
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Driving intention recognition and behaviour prediction based on a double-layer hidden Markov model[J]. 信息与电子工程前沿（英文）, 2012,13(3):208-217.

Lei HE, Chang-fu ZONG, Chang WANG. Driving intention recognition and behaviour prediction based on a double-layer hidden Markov model[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(3): 208-217.
Driving intention recognition and behaviour prediction based on a double-layer hidden Markov model[J]. 信息与电子工程前沿（英文）, 2012,13(3):208-217. DOI： 10.1631/jzus.C11a0195.

Lei HE, Chang-fu ZONG, Chang WANG. Driving intention recognition and behaviour prediction based on a double-layer hidden Markov model[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(3): 208-217. DOI： 10.1631/jzus.C11a0195.

摘要

Abstract

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.

关键词

Keywords

references

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