scholarly journals Network Management and Monitoring Solutions for Vehicular Networks: A Survey

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 853 ◽  
Author(s):  
João Dias ◽  
Joel Rodrigues ◽  
Vasco Soares ◽  
João Caldeira ◽  
Valery Korotaev ◽  
...  
Keyword(s):  
Vehicular Networks ◽  
Low Cost ◽  
Efficient Solutions ◽  
Transport Systems ◽  
Intelligent Transport Systems ◽  
Node Density ◽  
Intelligent Transport ◽  
Comprehensive Survey ◽  
Network Disruption ◽  
Open Issues

Vehicular networks are emerging as a promising technology that enables reliable and low-cost solutions for intelligent transport systems (ITSs), mainly due to their enormous potential to be considered for multiple purposes and scenarios. These networks are characterized by unique and challenging features such as packet fragmentation, low node density, short contact duration, and network disruption. These features may result in the absence of a path between the source and destination nodes, which is one of the most challenging issues faced by this type of network. To overcome some of these problems, it is necessary to provide vehicular networks with sophisticated tools or methodologies to implement monitoring and management operations. However, designing efficient solutions for this type of network is not an easy task due to its particular characteristics. This paper elaborates on a comprehensive survey focusing on promising proposals to deal with monitoring and management functionalities in vehicular networks. This work aims not only to present the state of the art on monitoring and management solutions but also to analyze their benefits and drawbacks, identify open issues, and provide guidelines for further contributions.

scholarly journals “Mass Centre” Vectorization Algorithm for Vehicle’s Counting Portable Video System

2016 ◽  
Vol 17 (4) ◽  
pp. 289-297
Author(s):  
Vladislav Gaidash ◽  
Alexander Grakovski
Keyword(s):  
Low Cost ◽  
Transport Infrastructure ◽  
Transport Systems ◽  
Intelligent Transport Systems ◽  
Basic Algorithm ◽  
Counting System ◽  
Object A ◽  
Intelligent Transport ◽  
Mass Centre ◽  
Mobile Vehicle

Abstract Vehicle counting is one of the most basic challenges during the development and establishment of Intelligent Transport Systems (ITS). The main reason for vehicle counting is the necessity of monitoring and maintaining the transport infrastructure, preventing different kind of faults such as traffic jams. The main applied solution to this problem is video surveillance, which is presented by different kind of systems. Some of these systems use a network of static traffic cameras, expensive for establish and maintain, or mobile units, fast for redeployment, but fewer in diversity. In this paper, one particular concept of a low-cost mobile vehicle counting system is investigated, which uses an object detection method based on calculating “mass centre” of detected features of possible object. A hypothesis of improvement of the basic algorithm was formulated and a modification was proposed. In order to prove the hypothesis, both basic and modified algorithms were tested and evaluated.

scholarly journals A Low-Cost Vehicular Infrastructure of Monitoring and Communicating Information for Intelligent Transport Systems

2019 ◽  
Vol 15 (7) ◽  
pp. 930-943
Author(s):  
Daniel Rodrigues ◽  
Jessica Nayara Severino ◽  
Andre Luis Cristiani ◽  
Fabiano Tadeu Mathias Costa ◽  
Luis Hideo Vasconcelos Nakamura ◽  
...  
Keyword(s):  
Low Cost ◽  
Transport Systems ◽  
Intelligent Transport Systems ◽  
Intelligent Transport

scholarly journals Intelligent transport systems in multimodal logistics: A case of role and contribution through wireless vehicular networks in a sea port location

2012 ◽  
Vol 137 (1) ◽  
pp. 165-175 ◽  
Author(s):  
Adrian E. Coronado Mondragon ◽  
Chandra S. Lalwani ◽  
Etienne S. Coronado Mondragon ◽  
Christian E. Coronado Mondragon ◽  
Kulwant S. Pawar
Keyword(s):  
Vehicular Networks ◽  
Transport Systems ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Port Location

scholarly journals The Need for Cooperative Automated Driving

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 754
Author(s):  
Jan Cedric Mertens ◽  
Christian Knies ◽  
Frank Diermeyer ◽  
Svenja Escherle ◽  
Sven Kraus
Keyword(s):  
Road Traffic ◽  
Efficient Solutions ◽  
Transport Systems ◽  
Automated Driving ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Cooperative Driving ◽  
Communication Techniques ◽  
Human Driver ◽  
Different Characteristics

In this paper we describe cooperation and social dilemmas in multiagent systems, with an analogy applied to road traffic. Cooperative human drivers, based on their perception of trust and fairness, find efficient solutions for such dilemmas. In the development of automated vehicles (AVs) it is therefore important to ensure that this cooperative ability is maintained even without a human driver. Therefore, the topic of cooperative intelligent transport systems (C-ITSs) is discussed in detail and different characteristics of cooperation and their implementation are derived. Further, three planning levels with the corresponding communication techniques are discussed and several methods for maneuver planning are listed. All in all, we hope that this paper will allow us to better classify different cooperative scenarios, develop novel approaches for cooperative AVs (CAVs), and emphasize the need for cooperative driving.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

ASSESSMENT OF THE READINESS OF THE OREL CITY AGGLOMERATION TO IMPLEMENT INTELLIGENT TRANSPORT SYSTEMS

2020 ◽  
Vol 70 (3) ◽  
pp. 64-71
Author(s):  
A.S. BODROV ◽  
◽  
M.V. KULEV ◽  
D.S. DEVYATINA ◽  
O.A. LOBYNTSEVA ◽  
...  
Keyword(s):  
Transport Systems ◽  
Intelligent Transport Systems ◽  
Intelligent Transport
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