scholarly journals On Medium Access and Physical Layer Standards for Cooperative Intelligent Transport Systems in Europe

2011 ◽  
Vol 99 (7) ◽  
pp. 1183-1188 ◽  
Author(s):  
Erik G. Strom
2016 ◽  
Vol 8 (2) ◽  
pp. 107 ◽  
Author(s):  
Cristian Roman ◽  
Ruizhi Liao ◽  
Peter Ball ◽  
Shumao Ou

The Intelligent Transport Systems (ITS) wireless infrastructure needs to support various safety and non-safety services for both autonomous and non-autonomous vehicles.The existing wireless infrastructures can already be used for communicating with different mobile entities at various monetary costs.A packet scheduler, included in a shim layer between the network layer and the medium access (MAC) layer, which is able to schedule packets between uncoordinated Radio Access Technologies (RATs) without modification of the wireless standards, has been devised and its performance evaluated.In this paper, we focus on the influence of mobility type in heterogeneous wireless networks.Three cases are considered based on the mobility in the city: walking, cycling, and driving. Realistic simulations are performed by generating mobility traces of Oxford from Google Maps and overlaying the real locations of existing WiFi Access Points. Results demonstrate that the shim layer approach can accommodate different user profiles and can be a useful abstraction to support Intelligent Transport Systems where there is no coordination between different wireless operators.


2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.

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.


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

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