Reinforcement Learning in Urban Network Traffic-signal Control

IF 1 Q4 ENGINEERING, CIVIL
Eslam Al-Kharabsheh
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引用次数: 0

Abstract

Traffic-signal recognition and anticipation are essential for advanced driver-assistance systems. Due to its superior performance in data categorization, deep learning has gained significance in vision-based object identification in recent years. When examining the application of deep learning to develop a high-performance urban traffic-signal detection system, the input image's colour space, as well as the deep-learning network model are examined as part of the system's primary components. Using distinct network models based on the Faster R-CNN algorithm and colour spaces in simulations helps the RGB (red, green and blue) colour space and the Faster R-CNN model detects the method of network target. A series of fundamental convolutional networks is used depending on pooling layers to extract the features of maps of images for training datasets, where the data may be used to develop a system for traffic-signal detection and create a new traffic signal that requires image recognition. KEYWORDS: Bounding boxes, Faster R-CNN, Modelled environments, Simulation, Traffic-signal detecting system.
城市网络交通信号控制中的强化学习
交通信号识别和预测对于先进的驾驶员辅助系统至关重要。由于其在数据分类方面的优异性能,近年来深度学习在基于视觉的目标识别中具有重要意义。在研究深度学习在开发高性能城市交通信号检测系统中的应用时,将输入图像的色彩空间以及深度学习网络模型作为系统主要组件的一部分进行检查。在模拟中使用基于Faster R-CNN算法和色彩空间的不同网络模型有助于RGB(红、绿、蓝)色彩空间,并且Faster R-CNN模型检测网络目标的方法。基于池化层,使用一系列基本卷积网络来提取训练数据集图像地图的特征,其中数据可用于开发交通信号检测系统,并创建需要图像识别的新交通信号。关键词:边界框,Faster R-CNN,模拟环境,仿真,交通信号检测系统。
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来源期刊
CiteScore
2.10
自引率
27.30%
发文量
0
期刊介绍: I am very pleased and honored to be appointed as an Editor-in-Chief of the Jordan Journal of Civil Engineering which enjoys an excellent reputation, both locally and internationally. Since development is the essence of life, I hope to continue developing this distinguished Journal, building on the effort of all the Editors-in-Chief and Editorial Board Members as well as Advisory Boards of the Journal since its establishment about a decade ago. I will do my best to focus on publishing high quality diverse articles and move forward in the indexing issue of the Journal.
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