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中文摘要：
我们建议模型结构，双层躲 Markov 模型(唔) 认出开车意愿并且预言开车行为。上面层多维分离唔(MDHMM ) 在双层唔在一个联合工作盒子中代表开车意愿，在降低层的多维的 Gaussian 在某些单个工作盒子中根据开车行为构造了唔(MGHMM ) 。开车行为被调遣驱动程序和车辆状态信息的信号认出，并且公认的结果被送到上面层唔认出开车意愿。另外，在不久的将来驾驶行为用可能性的最大值方法被预言。联合工作盒子上的即时开车模拟器测试证明双层唔能认出开车意愿并且精确地并且高效地预言开车行为。作为结果，模型为危险和改善舒适性能的警告前和干预提供基础。
英文摘要：
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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