为了提高呼吸信号判别驾驶疲劳的准确率，通过模拟驾驶试验探究呼吸信号与驾驶员疲劳状态的关系，提出呼吸疲劳节点的概念，并基于呼吸疲劳节点判别驾驶员的疲劳状态。首先，通过模拟驾驶试验采集驾驶员的呼吸信号，采用Karolinska嗜睡量表（Karolinska sleepiness scale, KSS）对疲劳程度进行主观自评量化。其次，把单位时间内眼睛闭合百分比（percentage of eyelid closure over the pupil over time, PERCLOS）作为参考，与主观自评反馈结合，对驾驶员呼吸疲劳节点进行标定。最后，基于呼吸疲劳节点利用随机树算法（random tree, RT）获得轻/重度呼吸疲劳变化节点的判别模型。结果表明：该模型能更加及时、准确地判别出驾驶员的疲劳状态；基于随机树算法获得的筛选条件对轻度呼吸疲劳变化节点识别的准确性要高于重度呼吸疲劳变化节点；轻/重度呼吸疲劳变化节点的平均识别误差分别为3.50 min和3.66 min，预测准确率分别为92.09%和92.03%。

In order to improve the accuracy of respiratory signal to distinguish driving fatigue, the relationship was explored between respiratory signal and driver fatigue state through simulated driving test. The concept of respiratory fatigue node was proposed, and the driver's fatigue state was distinguished based on the respiratory fatigue node. Firstly, the respiratory signals of drivers were collected by simulated driving test, and the Karolinska sleepiness scale (KSS) was used to quantify the subjective self-evaluation of their fatigue degree. Secondly, the percentage of eyelid closure over the pupil over time (PERCLOS) was used as a reference, combined with subjective self-evaluation feedback to calibrate the respiratory fatigue nodes of drivers. Finally, based on the respiratory fatigue nodes, the random tree (RT) algorithm was used to obtain the discriminant model of mild/severe respiratory fatigue change nodes. The results show that the model can identify the driver's respiratory fatigue state more timely and accurately and the accuracy of the screening conditions based on the random tree algorithm for the identification of mild respiratory fatigue change nodes is higher than that of severe respiratory fatigue change nodes. The average recognition errors of mild/severe respiratory fatigue change nodes are 3.50 min and 3.66 min, respectively, the prediction accuracy is 92.09% and 92.03% respectively.