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基于呼吸疲劳节点的驾驶员疲劳状态判别

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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％.
Driver Fatigue State Discrimination Based on Respiratory Fatigue Node
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 de-gree.Secondly,the percentage of eyelid closure over the pupil over time(PERCLOS)was used as a reference,combined with subjec-tive 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 con-ditions based on the random tree algorithm for the identification of mild respiratory fatigue change nodes is higher than that of severe re-spiratory 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.

driving fatiguerespiratory signalrespiratory fatigue noderandom tree(RT)algorithmfatigue state discrimination

朱靖龙、刘强、李波、陈泽平、赵国亮

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中山大学智能工程学院,深圳 518107

广东玛西尔电动科技有限公司,肇庆 523268

广州汽车集团股份有限公司汽车工程研究院,广州 511434

驾驶疲劳 呼吸信号 呼吸疲劳节点 随机树算法(RT) 疲劳状态判别

广东省基础与应用基础研究基金广东省重点领域研发计划项目教育部产学合作协同育人项目肇庆市西江创新创业团队与领军人才项目广东省本科高校教学质量与教学改革工程建设项目

2022A15150106922022B0701180001220605329072033

2024

10.12404/j.issn.1671-1815.2304837

科学技术与工程
中国技术经济学会

科学技术与工程

CSTPCD北大核心
影响因子：0.338
ISSN：1671-1815
年,卷(期)：2024.24(16)