<?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.2025060029</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>交通工程建设中多源融合感知技术的应用与优化探讨</title><url>https://artdesignp.com/journal/ME/2/6/10.61369/ME.2025060029</url><author>郭大鹏</author><pub-date pub-type="publication-year"><year>2025</year></pub-date><volume>2</volume><issue>6</issue><history><date date-type="pub"><published-time>2025-06-20</published-time></date></history><abstract>随着城市化与智能网联汽车技术的发展，传统单一传感器已无法满足交通工程对感知全面性、准确性与实时性的需求。本文以多源融合感知技术为研究对象，通过文献调研与案例分析，阐述其基本原理与分类，剖析在高速公路、城市交通、智能网联汽车及特殊环境中的应用场景，梳理关键算法与优化策略，指出当前面临的数据异构性、计算复杂度等挑战，并展望未来发展趋势。研究表明，多源融合感知技术可有效弥补单一传感器局限，为智能交通系统提供核心支撑，未来需进一步推进技术融合与标准化建设。</abstract><keywords>多源融合感知,交通工程,传感器,智能交通,数据融合算法</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>&amp;nbsp;[1]Cognitive Disentanglement for Referring Multi-Object Tracking[EB/OL].https://arxiv.org/pdf/2503.11496,2025.&amp;nbsp;[2]Wei Liu.UniMSF:A Unified Multi-Sensor Fusion Framework for Intelligent Transportation System Global Localization[EB/OL].https://arxiv.org/pdf/2409.12426,2024.&amp;nbsp;[3]罗如意.智慧高速多源异构感知数据实时高精度融合算法[J].中国交通信息化,2024(4):11-15.&amp;nbsp;[4]视频孪生技术赋能智慧交管建设[EB/OL].https://blog.csdn.net/asiafinance/article/details/146507314,2025.&amp;nbsp;[5]Kyocera.Revolutionizing Detection:Kyocera Unveils the World&amp;rsquo;s First Camera-LIDAR Fusion Sensor[EB/OL].https://americas.kyocera.com/news/2025/01/07113743.html,2025.&amp;nbsp;[6]BEVFusion:Multi-Task Multi-Sensor Fusion with Unified Bird&amp;rsquo;s-Eye View Representation[EB/OL].https://arxiv.org/pdf/2205.13542,2022.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
