论文标题
使用长期经常性卷积网络检测驾驶员的注意力
Detecting Driver's Distraction using Long-term Recurrent Convolutional Network
论文作者
论文摘要
在这项研究中，我们展示了一种新型的大脑计算机界面（BCI）方法，可检测驾驶员分心事件以提高道路安全性。我们使用一种商业无线耳机，该耳机从大脑中产生脑电图信号。我们从参与者那里收集了真正的脑电图信号，他们进行了40分钟的驾驶模拟，并被要求在驾驶时执行不同的任务。这些信号被分割为短窗口，并使用时间序列分类（TSC）模型进行标记。我们研究了不同的TSC方法，并为此任务设计了一个长期的卷积网络（LCRN）模型。我们的结果表明，我们的LRCN模型在检测驾驶员分心事件时的性能优于TSC模型的最佳状态。
In this study we demonstrate a novel Brain Computer Interface (BCI) approach to detect driver distraction events to improve road safety. We use a commercial wireless headset that generates EEG signals from the brain. We collected real EEG signals from participants who undertook a 40-minute driving simulation and were required to perform different tasks while driving. These signals are segmented into short windows and labelled using a time series classification (TSC) model. We studied different TSC approaches and designed a Long-term Recurrent Convolutional Network (LCRN) model for this task. Our results showed that our LRCN model performs better than the state of the art TSC models at detecting driver distraction events.
