Driving intention recognition and behaviour prediction based on a double-layer hidden Markov model |
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Authors: | Lei HE Chang-fu ZONG Chang WANG State |
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Affiliation: | Lei HE, Chang-fu ZONG, Chang WANG (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China) |
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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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Keywords: | Vehicle engineering Driving intention recognition Driving behaviour prediction Driver model Double-layer hidden Markov model (HMM) |
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