基于电生理的机器学习在驾驶疲劳识别中的应用
Application of electrophysiology-based machine learning in identifying driving fatigue
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摘要:
道路交通事故（RTA）导致大量人员伤亡和财产损失，驾驶疲劳是导致RTA的重要因素之一。电生理信号作为神经系统调节身体机能的一种信息反馈，能够反映出驾驶员的疲劳状态，但目前针对电生理信号作为信息输入并采用机器学习的方法进行驾驶疲劳识别的相关研究缺乏系统综述。通过调研疲劳相关文献，本文总结疲劳的神经调节机制，阐明驾驶疲劳是由于心理及生理的双重负荷所导致并汇总驾驶疲劳相关的诱发因素，归纳当前与驾驶疲劳识别相关的电生理信号及其生理机制和相关指标。机器学习算法被广泛应用于驾驶疲劳识别中，本文汇总现有研究中以电生理信号作为信息输入源并采用各种机器学习算法构建的驾驶疲劳识别模型，比较各种机器学习算法的有效性，介绍有监督机器学习的优缺点；并指出应用于驾驶疲劳识别时应根据样本情况和模型特征值选取合适的分类算法；多种电生理信号作为信息源有助于提高疲劳识别模型的准确性，但模型输入特征值增加并不能有效提高模型准确性。最后指出基于电生理信号的疲劳识别方法的研究进展，为驾驶疲劳识别提供了新机遇。
Abstract: Road traffic accidents (RTA) can cause a large number of casualties and property losses. Driving fatigue is one of the important factors leading to RTA. Electrophysiological signals, as a kind of information feedback for the nervous system to regulate body functions, can reflect drivers’ fatigue state. However, there is a lack of systematic reviews on the current research on electrophysiological signals as information input of machine learning methods for driving fatigue recognition. By investigating fatigue-related literature, the current paper summarized the neural regulation mechanism of fatigue, clarified that driving fatigue is caused by both psychological and physiological loads, recognized inducing factors related to driving fatigue, and summed up electrophysiological signals now in use of driving fatigue recognition, as well as their physiological mechanisms and related indicators. Machine learning algorithms are widely used in identifying driving fatigue. Based on existing studies that used electrophysiological signals as information input source and applied various machine learning algorithms to build driving fatigue identification models, this paper compared the effectiveness of various machine learning algorithms, and described the advantages and disadvantages of supervised machine learning. It is pointed out that suitable classification algorithms should be selected according to sample conditions and model eigenvalues when applied to driving fatigue recognition. In addition, a variety of electrophysiological signals as information sources can help improve the accuracy of a fatigue recognition model, but the increase of model input eigenvalues cannot. Finally, the research progress of identification methods based on electrophysiological signals provided new opportunities for identifying driving fatigue.
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