Research on the internet of vehicles assisted traffic management systems for observing traffic density. (July 2022)

• Record Type: Journal Article
• Authors: Saad, Aldosary; Shalaby, Ahmed; Mohamed, Abdallah A.
• Is Part Of: Computers & electrical engineering. Volume 101(2022)
• Publication Date: 2022-07
• Subjects: Computer vision -- Internet of vehicles -- Convolution neural network -- Human-machine interaction -- Navigation assistance -- Traffic management -- Interactive applications -- Artificial intelligence -- Navigations -- Time intervals; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108100
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Computer vision (CV) in a modern smart city is to be relied upon to deliver interactive and user-satisfying applications. CV is responsible for identifying humans and objects, among others, through which interactive applications are designed. The interacting machine handles visual inputs and processes them to provide quality responses to users. An artificial intelligence (AI) field known as computer vision (CV) is used to train computers in real-world applications to interact with the visual world. The internet of vehicles assisted traffic management system based on computer vision ((IoVTMS-CV) roadside pedestrian process is discussed to monitor and evaluate human movements depending on their location and deviation from previous time intervals. Traffic density and observations were interactively shared with the vehicles through navigation assistance based on these detections. In this method, convolutional neural learning has been used to identify the deviations over different time intervals. The variations were identified as incoherent with the standard patterns defined for roadside traffic management. The experiment's results show it's at least 37% more effective than traditional methods. Graphical abstract: Image, graphical abstract