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Updated: Jan 19, 2026

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DrowsyDG-Phys:在使用生理信号的条件自动化车辆中,可概括的驾驶员昏昏欲睡的估计.

Jiyao Wang¹, Wenbo Li², Zhenyu Wang¹

¹Systems Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China.

Accident; analysis and prevention

|January 17, 2026

概括

驾驶员昏昏欲睡的检测得到了改进,使用了一个新的域泛化框架DrowsyDG-Phys. 该模型使用生理信号进行更强大和更普遍的驾驶员监控,提高道路安全.

关键词:

域名通用化域名通用化司机昏昏欲睡的估计司机昏昏欲睡的估计神经网络的神经网络生理信号生理信号

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科学领域:

物理计算的物理计算.
机器学习机器学习
道路安全工程工程道路安全工程

背景情况:

司机昏昏欲睡是道路交通事故的主要原因之一.
传统的嗜睡检测方法缺乏稳定性和灵活性.
深度学习模型在现实世界条件下与领域转移作斗争.

研究的目的:

提出DrowsyDG-Phys,这是一个用于检测司机昏昏欲睡的新型域泛化框架.
通过使用生理信号来提高昏昏欲睡检测的概括性和稳定性.
建立一个多源域概括基准,用于驾驶员的昏昏欲睡.

主要方法:

使用心电图,皮肤活动和呼吸信号.
开发了一个用于时间和频率域特征学习的骨干网络.
集成了三个新的损失功能:对比调整,特征集中和昏昏欲睡评估对齐.

主要成果:

在域泛化协议上实现了78.5%的准确性.
在跨主题协议中获得了88.4%的准确性.
在概括和稳定性方面,DrowsyDG-Phys的表现优于基线方法.

结论:

拟议的DrowsyDG-Phys框架显著改善了驾驶员昏昏欲睡的检测.
该模型在各种数据集和条件中展示了增强的概括性.
这种方法使得基于生理信号的嗜睡监测变得更加可靠.