基于物联网的智能框架,为两轮车安全驾驶体验提供了安全的驾驶体验
Gunjan Chhabra1,2, Keshav Kaushik3, Pardeep Singh4
1Department of Computer Science and Engineering, Graphic Era Hill University, Dehradun, Uttarakhand, India.
Scientific reports
|September 18, 2024
概括
这项研究引入了一个智能系统,使用电脑电图 (EEG) 信号来预测酒精,头盔使用和速度,旨在以95%的准确率减少道路交通事故死亡人数.
科学领域:
- 神经科学是一个神经科学.
- 道路安全工程 道路安全工程
- 人工智能的人工智能
背景情况:
- 电脑电图 (EEG) 信号对于理解大脑活动至关重要,并且可以表明酒精.
- 道路交通事故,通常涉及驾驶障碍和缺少使用头盔,构成重大公共健康风险.
- 目前用于实时监测驾驶员行为和损伤的方法有限.
研究的目的:
- 开发一个智能系统,实时预测驾驶员的酒精水平,头盔的使用和车辆的速度.
- 利用EEG信号分析来提高道路安全和预防事故.
- 创建一个智能决策模型,以减轻道路交通事故死亡人数.
主要方法:
- 使用多层感知子来处理信号.
- 用于从EEG信号中提取特征的面向梯度 (HoG) 组图.
- 实现了一个随机森林分类器,用于实时决策和预测.
主要成果:
- 在预测与道路安全相关的关键参数方面获得了大约95%的准确性.
- 在各种室内和室外工作环境中表现出令人满意的性能.
- 该系统有效地将EEG分析与机器学习相结合,用于智能监控.
结论:
- 拟议的智能系统显示出在减少道路交通事故死亡人数方面的巨大潜力.
- 使用EEG实时预测酒精和驾驶员行为是可行的和准确的.
- 这项技术提供了一种有前途的方法,通过先进的监控来提高道路安全.
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