Intelligent transport systems. Guidance protocol via personal ITS station for advisory safety systems

2015 ◽  
Keyword(s):  
Transport Systems ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Safety Systems

scholarly journals IMPROVEMENT OF ROAD SAFETY USING PASSIVE AND ACTIVE INTELLIGENT VEHICLE SAFETY SYSTEMS

Transport ◽  
2007 ◽  
Vol 22 (4) ◽  
pp. 284-289 ◽  
Author(s):  
Aldona Jarašūnienė ◽  
Gražvydas Jakubauskas
Keyword(s):  
Road Safety ◽  
White Paper ◽  
Transport Systems ◽  
Vehicle Safety ◽  
Intelligent Vehicle ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Mode Of Transport ◽  
Safety Systems ◽  
Safety Applications

Following the measures foreseen in the Transport White Paper 2001, situation of road safety has improved. Road fatalities have declined by more than 17 % since 2001 in the EU. However, with around 41 600 deaths and more than 1.7 million injured in 2005, road remains the least safe mode of transport and objectives to halve the number of fatalities on road by 2010 is most likely not feasible to achieve. Therefore a need for the intelligent vehicle safety systems, that enable to raise the level of road safety, is much higher than ever before. The Intelligent Vehicle Safety Systems ensure a superior safety on road would it be vehicle‐based or infrastructure‐related systems. These can be divided into passive and active safety applications where the former help people stay alive and uninjured in a crash, while the latter help drivers to avoid accidents. Some of the most promising (e‐call) and the most used (ABS, ESP) systems are analised more specifically in the paper. Possible solutions to deploying intelligent transport systems in Lithuania are also introduced.

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.

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