Forklift safety, traffic engineering and intelligent transport systems: a case study

2004 ◽  
Vol 35 (6) ◽  
pp. 575-581 ◽  
Author(s):  
Tim Horberry ◽  
Tore J Larsson ◽  
Ian Johnston ◽  
John Lambert
Author(s):  
Ronald Schroeter ◽  
Alessandro Soro ◽  
Andry Rakotonirainy

Intelligent Transport Systems (ITS) encompass sensing technologies, wireless communication, and intelligent algorithms, and resemble the infrastructure for ubiquitous computing in the car. This chapter borrows from social media, locative media, mobile technologies, and urban informatics research to explore three classes of ITS applications in which human behavior plays a more pivotal role. Applications for enhancing self-awareness could positively influence driver behavior, both in real-time and over time. Additionally, tools capable of supporting our social awareness while driving could change our attitude towards others and make it easier and safer to share the road. Lastly, a better urban awareness in and outside the car improves our understanding of the road infrastructure as a whole. As a case study, the authors discuss emotion recognition (emotions such as aggressiveness and anger are a major contributing factor to car crashes) and a suitable basis and first step towards further exploring the three levels of awareness, self-, social-, and urban-awareness, in the context of driving on roads.


2021 ◽  
Vol 110 ◽  
pp. 63-69
Author(s):  
Celestin Drăgănescu

The paper proposes a dynamic adaptive framework that responds to the needs of context-aware services provided by the intelligent transport systems. The executive core of this framework is a software architecture model that ensures the running of an application which is adaptable to context changes. The solution was tested on a case study that highlights provided contextual information through an inter-vehicle communication system.


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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