PDF(Pubmed)
Abstract：
The driver in road hypnosis has not only some external characteristics, but also some internal characteristics. External features have obvious manifestations and can be directly observed. Internal features do not have obvious manifestations and cannot be directly observed. They need to be measured with specific instruments. Electroencephalography (EEG), as an internal feature of drivers, is the golden parameter for drivers\' life identification. EEG is of great significance for the identification of road hypnosis. An identification method for road hypnosis based on human EEG data is proposed in this paper. EEG data on drivers in road hypnosis can be collected through vehicle driving experiments and virtual driving experiments. The collected data are preprocessed with the PSD (power spectral density) method, and EEG characteristics are extracted. The neural networks EEGNet, RNN, and LSTM are used to train the road hypnosis identification model. It is shown from the results that the model based on EEGNet has the best performance in terms of identification for road hypnosis, with an accuracy of 93.01%. The effectiveness and accuracy of the identification for road hypnosis are improved in this study. The essential characteristics for road hypnosis are also revealed. This is of great significance for improving the safety level of intelligent vehicles and reducing the number of traffic accidents caused by road hypnosis.
摘要：
道路催眠中的驾驶员不仅具有某些外部特征，但也有一些内在特征。外部特征有明显的表现，可以直接观察到。内部特征没有明显的表现，不能直接观察。它们需要用特定的仪器进行测量。脑电图(EEG),作为驱动程序的内部特征，是驾驶员寿命识别的黄金参数。脑电图对道路催眠的辨认具有主要意义。提出了一种基于人体脑电数据的道路催眠识别方法。通过车辆驾驶实验和虚拟驾驶实验可以收集道路催眠中驾驶员的脑电数据。用PSD(功率谱密度)方法对采集的数据进行预处理,并提取脑电图特征。神经网络EEGNet，RNN,和LSTM用于训练道路催眠识别模型。结果表明，基于EEGNet的模型在道路催眠识别方面具有最佳性能，准确率为93.01%。本研究提高了道路催眠识别的有效性和准确性。还揭示了道路催眠的基本特征。这对于提高智能车辆的安全水平,减少道路催眠引发的交通事故数量具有重要意义。
