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首页> 外文期刊>Intelligent Transport Systems, IET >Driver fatigue evaluation model with integration of multi-indicators based on dynamic Bayesian network
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Driver fatigue evaluation model with integration of multi-indicators based on dynamic Bayesian network

机译：基于动态贝叶斯网络的多指标集成驾驶员疲劳评价模型

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摘要

Electroencephalogram (EEG) data are an effective indicator to evaluate driver fatigue, but it is usually disturbed by noise. The frequent head nodding, as well as the time of day and total driving time, also have very close relationship with driver fatigue. All these factors should be taken into account for comprehensive driver fatigue evaluation. 50 drivers are recruited to take part in the fatigue-oriented experiment on the driving simulator. Based on the EEG samples, the EEG-based indicator of driver fatigue has been established by artificial neural network. Subsequently, a new algorithm is present to compute the head nodding angle with posture data from the passive tools fixed on the driver's head and trunk, respectively, and then head-based indicator of driver fatigue is determined. Finally, a new evaluation model of driver fatigue is established with integration of four fatigue-based indicators with DBN (Dynamic Bayesian Network). The results show that it is more accurate to evaluate the driver fatigue compared with the sole EEG-based indicator.
机译：脑电图（EEG）数据是评估驾驶员疲劳程度的有效指标，但通常会受到噪音的干扰。频繁的头部点头，以及一天中的时间和总驾驶时间，也与驾驶员疲劳感有着非常密切的关系。所有这些因素应综合考虑驾驶员疲劳程度。招募了50名驾驶员在驾驶模拟器上参加了针对疲劳的实验。基于脑电图样本，通过人工神经网络建立了基于脑电图的驾驶员疲劳指标。随后，提出了一种新算法，利用固定在驾驶员头部和躯干上的被动工具的姿势数据计算头部点头角度，然后确定驾驶员疲劳的基于头部的指标。最后，通过将四个基于疲劳的指标与DBN（动态贝叶斯网络）相集成，建立了驾驶员疲劳的新评估模型。结果表明，与唯一的基于EEG的指标相比，评估驾驶员疲劳更为准确。

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