驾驶员情绪识别使用多式联接信号,通过结合调节器和自动调节器
Weiguang Wang1, Jian Lian1, Chuanjie Xu1
1School of Intelligence Engineering, Shandong Management University 250357, Jinan, P. R. China.
International journal of neural systems
|September 25, 2025
概括
这项研究开发了一种用于驾驶员情绪识别的多式系统,集成面部表情,电心电图 (ECG) 和电脑电图 (EEG) 信号,以提高驾驶员监控的准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 驾驶员情绪识别对于车辆安全和先进的驾驶辅助系统至关重要.
- 现有的方法往往依赖于单一的模式,限制了准确性和可靠性.
- 整合多种数据源可以更全面地了解驾驶员的情绪状态.
研究的目的:
- 开发一种用于准确识别驾驶员情绪的多式联运系统.
- 整合面部表情,心电图和脑电图信号,以进行强大的情绪检测.
- 通过改进情绪状态识别来增强驾驶员监控系统.
主要方法:
- 一个Conformer模型被用来分析面部图像的视觉情绪线索.
- 两个Autoformer模型处理了电心电图 (ECG) 和电脑电图 (EEG) 信号.
- 一个交叉注意力机制融合了所有三种模式的嵌入,然后进行分类.
主要成果:
- 视觉,生理 (ECG) 和神经 (EEG) 数据的融合显著提高了情绪检测的准确性和可靠性.
- 与单一模式方法相比,多式模式方法显示出更高的性能.
- 该系统在驾驶过程中有效地识别了驾驶员的情绪状态.
结论:
- 多模式集成为先进的驾驶员情绪识别提供了一个有前途的方法.
- 拟议的系统增强了对驾驶员安全相关的情绪过程的洞察力.
- 这项工作为未来在情感计算和智能交通系统方面的进步提供了基础.
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