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在模拟驾驶过程中基于EEG的紧急制动意向检测.

Xinbin Liang1, Yang Yu1, Yadong Liu2

  • 1College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, Hunan, China.

Biomedical engineering online
|July 1, 2023
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概括

使用脑电图 (EEG) 信号检测紧急制动意图是可行的. 像深度学习这样的先进方法在区分紧急情况和正常制动方面表现有前途,使车辆能够更早地响应并避免碰撞.

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大脑与计算机接口 (BCI)检测 检测 检测 检测 检测电脑电图 (EEG) 是一个电脑电图.紧急制动的意图是紧急制动.模拟驾驶的模拟驾驶方式

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科学领域:

  • 神经科学与人与计算机的交互
  • 驾驶员监控系统 驾驶员监控系统
  • 汽车安全技术 汽车安全技术

背景情况:

  • 目前基于脑电图 (EEG) 的驾驶员紧急车意图检测主要区分紧急情况和正常驾驶.
  • 关于使用EEG区分紧急制动与正常制动的研究有限.
  • 现有的方法依赖于传统的机器学习,手动提取功能.

研究的目的:

  • 提出一种基于EEG的新策略,用于检测驾驶员的紧急制动意图.
  • 为了比较传统的,基于里曼的几何和深度学习的方法来完成这个检测任务.
  • 使用原始EEG信号作为输入,绕过手动特征提取.

主要方法:

  • 在模拟驾驶平台上进行的实验,以正常驾驶,正常制动和紧急制动场景进行.
  • 分析EEG特征图,以确定制动模式之间的差异.
  • 应用传统的,基于里曼的几何和深度学习算法,对原始EEG信号进行意图预测.

主要成果:

  • 基于里曼的几何和深度学习的方法都超过了传统的方法.
  • 基于深度学习的EEGNet算法实现了0.91的曲线下面面积 (AUC) 和0.85的F1得分,以区分紧急情况和正常制动在200 ms之前.
  • 在应急制动和正常制动之间观察到EEG特征图的显著差异.

结论:

  • 从EEG信号中检测紧急制动意图是可行的,将其与正常驾驶和正常制动区分开来.
  • 准确识别紧急制动的意图允许更早地激活车辆制动系统.
  • 这项技术为人类与车辆的共同驾驶提供了一个以用户为中心的框架,有可能防止严重的碰撞.