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超宽带雷达授权驾驶员昏昏欲睡的检测与卷积空间特征工程和人工智能.

Hafeez Ur Rehman Siddiqui¹, Ambreen Akmal¹, Muhammad Iqbal²

¹Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Abu Dhabi Road, Rahim Yar Khan 64200, Punjab, Pakistan.

Sensors (Basel, Switzerland)

|June 27, 2024

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这项研究引入了一种人工智能驱动的系统,用于使用雷达数据检测驾驶员的昏昏欲睡. 先进的RF-XGB-SVM模型实现了99.58%的精度,显著提高了道路安全.

科学领域:

人工智能的人工智能
机器学习机器学习
计算机视觉计算机视觉
汽车安全汽车安全

背景情况:

昏昏欲睡的驾驶带来了相当大的风险,包括认知功能受损和增加事故的可能性,导致严重的结果,如伤害或死亡.
人工智能 (AI) 为实时驾驶员昏昏欲睡的检测提供了一个有前途的解决方案,旨在防止事故并提高驾驶员的性能.
迫切需要准确和及时的昏昏欲睡检测系统,以减少与疲劳有关的道路交通事故的发生率.

研究的目的:

开发和评估基于人工智能的系统,以准确,实时检测驾驶员的昏昏欲睡.
调查通过深度学习和机器学习模型处理的超宽带雷达数据的有效性,以检测昏昏欲睡.
通过数据增强技术,提高嗜睡检测模型的性能.

主要方法:

超宽带 (UWB) 雷达数据在五分钟间隔内收集,并分成一分钟图像块.
采用二维卷积神经网络 (2D-CNN) 来从灰度雷达图像中提取空间特征.
提取的特征被用来训练和测试各种机器学习分类器,包括组合模型 (RF-XGB-SVM) 结合随机森林,XGBoost和支持矢量机器.
用生成对抗网络 (GAN) 来增强数据以提高模型的准确性.

主要成果:

关键词:

卷积神经网络是一种卷积神经网络.昏昏欲睡的昏昏欲睡的昏昏欲睡的昏昏欲睡的组合模型组合模型组合模型空间特征空间特征空间特征超宽带雷达是一种超宽带雷达.

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最初的组合分类器RF-XGB-SVM的准确度为96.6%,k倍交叉验证得分为97%,低标准偏差为0.018.
使用生成对抗网络 (GAN) 的数据增强导致所有测试模型的精度提高.
增强数据集进一步提高了RF-XGB-SVM模型的性能,最终准确率达到99.58%.

结论:

拟议的AI方法有效地使用处理的UWB雷达数据检测驾驶员的昏昏欲睡.
整体模型RF-XGB-SVM,特别是在基于GAN的数据增强后,证明了对实时嗜睡检测的高准确性和稳定性.
这项研究强调了人工智能和雷达技术在开发先进的驾驶辅助系统方面的潜力,以显著提高道路安全.