Scholarlink

<p>This study presents a new system for managing traffic in real-time using YOLOv8 detection technology. The system dynamically adjusts traffic signals when emergency vehicles are detected, ensuring smoother traffic flow and improved road safety. By processing data from multiple cameras with advanced deep learning algorithms, it can make fast and adaptive decisions. Tests in different emergency situations showed its effectiveness in prioritizing emergency vehicles while maintaining regular traffic flow. Although promising, the system has limitations, including challenges with weather conditions and highly congested scenarios. Future work will focus on improving predictive accuracy by integrating additional data sources. As cities continue to grow and evolve, this work paves the way for future innovations in emergency vehicle prioritization, contributing to safer and more efficient urban mobility.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Real-time traffic signal adjustment using YOLOv8 for improved integration of emergency vehicles in smart traffic systems

  • 期刊论文

  • 发表日期：

    2025年5月12日

查看全文
  • Ali Omari Alaoui,
  • Mariame Oumoulylte,
  • Houda Oubalahcen,
  • Mohamed Khalifa Boutahir,
  • Abdelaaziz Hessane,
  • Ahmad El Allaoui

摘要

This study presents a new system for managing traffic in real-time using YOLOv8 detection technology. The system dynamically adjusts traffic signals when emergency vehicles are detected, ensuring smoother traffic flow and improved road safety. By processing data from multiple cameras with advanced deep learning algorithms, it can make fast and adaptive decisions. Tests in different emergency situations showed its effectiveness in prioritizing emergency vehicles while maintaining regular traffic flow. Although promising, the system has limitations, including challenges with weather conditions and highly congested scenarios. Future work will focus on improving predictive accuracy by integrating additional data sources. As cities continue to grow and evolve, this work paves the way for future innovations in emergency vehicle prioritization, contributing to safer and more efficient urban mobility.