<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">ME</journal-id><journal-title-group><journal-title>Modern Engineering</journal-title></journal-title-group><issn>2996-6973</issn><eissn>2996-6981</eissn><publisher><publisher-name>Art and Design</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/ME.2025050036</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>基于边缘计算对驾驶人状态监测系统的优化设计</title><url>https://artdesignp.com/journal/ME/2/5/10.61369/ME.2025050036</url><author>刘若涵,李战东,刘思源,韦周慧,赵安阳</author><pub-date pub-type="publication-year"><year>2025</year></pub-date><volume>2</volume><issue>5</issue><history><date date-type="pub"><published-time>2025-05-20</published-time></date></history><abstract>针对我国道路交通安全中疲劳驾驶引发事故的严峻问题，本文设计了一套基于边缘计算的驾驶人监测优化系统。该系统以头面部特征为核心监测依据，通过改进的 MTCNN模型实现人脸关键点精准定位，结合轻量化 AlexNet模型与Informer框架完成驾驶人状态识别与疲劳检测，并依托 ErgoAI Server边缘服务器实现数据实时处理与预警。实验基于 NTHU-DDD数据集验证，实验验证表明，系统在复杂驾驶环境下仍具备高准确率与快速响应能力，能为驾驶安全提供有力保障，同时为绿色智慧交通发展提供技术支撑。</abstract><keywords>驾驶人监测,边缘计算,疲劳检测,MTCNN模型,Informer框架</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] 李伟，何其昌，范秀敏. 基于汽车操纵信号的驾驶员疲劳状态检测[J]. 上海交通大学学报，2010,44（2）：292-296.[2] 马世伟，王泽敏，吕宝粮. 基于脑电信号的动车组司机疲劳状态评估技术研究[J]. 铁路节能环保与安全卫生，2021,11（4）：43-49.
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