使用卷积神经网络对主要和次要汽车驾驶员活动进行基于传感器的分类
Rafał Doniec1, Justyna Konior1, Szymon Sieciński1,2
1Department of Biosensors and Processing of Biomedical Signals, Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
这项研究引入了一种使用电眼图 (EOG) 信号和1D CNN来分类驾驶活动的新方法. 该系统实现了高精度,证明了提高驾驶员安全系统的潜力.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 汽车工程 汽车工程
背景情况:
- 司机的安全依赖于情境意识和适应能力.
- 现有的研究往往侧重于驾驶员行为异常和认知监测.
- 识别基本的驾驶活动对于先进的驾驶辅助系统至关重要.
研究的目的:
- 使用电眼图 (EOG) 信号开发基本驾驶活动的分类器.
- 将日常生活活动识别的方法适应驾驶环境.
- 为此任务评估1D卷积神经网络 (1D CNN) 的性能.
主要方法:
- 使用电眼图 (EOG) 信号来测量眼睛的运动.
- 采用一维卷积神经网络 (1D CNN) 来进行活动分类.
- 在16个主要和次要驾驶活动中训练和测试了分类器.
主要成果:
- 整体分类器在16个驾驶活动中实现了80%的准确性.
- 具体的驾驶活动,如交叉道路,停车场和环形车道显示高精度 (97.9%,96.8%,97.4%).
- 二级驾驶活动的F1得分为0.99,超过了主要活动 (0.93-0.94).
结论:
- 拟议的基于EOG的1D CNN分类器对于识别基本的驾驶活动是有效的.
- 该系统在特定的机动和次要行动中表现出高精度.
- 这种方法有望融入驾驶员安全和监控系统.
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