Deep Learning-Based Control System Design for Emergency Vehicles through Intersections  

作　　者：Dingru Li Yinghui He Yuanbo Yang Jiale Xu 

机构地区：[1]College of Civil Engineering and Transportation,Northeast Forestry University,Harbin 150040,China [2]School of Software,College of Computer Information Science,Southwest University,Chongqing 400715,China

出　　处：《Journal of Electronic Research and Application》2024年第6期208-221,共14页电子研究与应用

摘　　要：This paper addresses the challenge of integrating priority passage for emergency vehicles with optimal intersection control in modern urban traffic. It proposes an innovative strategy based on deep learning to enable emergency vehicles to pass through intersections efficiently and safely. The research aims to develop a deep learning model that utilizes intersection violation monitoring cameras to identify emergency vehicles in real time. This system adjusts traffic signals to ensure the rapid passage of emergency vehicles while simultaneously optimizing the overall efficiency of the traffic system. In this study, OpenCV is used in combination with Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to jointly complete complex image processing and analysis tasks, to realize the purpose of fast travel of emergency vehicles. At the end of this study, the principle of the You Only Look Once (YOLO) algorithm can be used to design a website and a mobile phone application (app) to enable private vehicles with emergency needs to realize emergency passage through the application, which is also of great significance to improve the overall level of urban traffic management, reduce traffic congestion and promote the development of related technologies.

关 键 词：Emergency vehicle priority Deep learning Signal light adjustment Traffic congestion reduction Trajectory optimization 

分 类 号：TN9[电子电信—信息与通信工程]