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

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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.

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Authors and Affiliations

  1. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, 130025, China

    Lei He, Chang-fu Zong & Chang Wang

Authors
  1. Lei He
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  2. Chang-fu Zong
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  3. Chang Wang
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Correspondence to Chang-fu Zong.

Additional information

Project (Nos. 50775096 and 51075176) supported by the National Natural Science Foundation of China

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He, L., Zong, Cf. & Wang, C. Driving intention recognition and behaviour prediction based on a double-layer hidden Markov model. J. Zhejiang Univ. - Sci. C 13, 208–217 (2012). https://doi.org/10.1631/jzus.C11a0195

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  • DOI: https://doi.org/10.1631/jzus.C11a0195

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