Intelligent transport systems. Integrated transport information, management and control. Data quality in ITS systems

This paper mainly studies Driving Assistance Systems and Detection Pedestrian Crossings of traffic and control, many years around the world and company studies have been conducted on intelligent transport systems (ITS). Intelligent vehicle, (IV) the system is part of a system which is designed to assist drivers in the perception of any dangerous situations before, to avoid accidents after sensing and understanding the environment around it. Methodology: we made an analysis of the peculiarities of the task of surveillance for pedestrian crossings and presented a detection system which these features into account. The system consists of a detector based on histograms of oriented gradients, and activity detector. The proposed Results tested detection precision and performance of the proposed system. The motion of the work is to combine the proposed system and the tracker. The results show that an adequate application of the quality and performance of the developed algorithm of detection of objects of interest in the work.

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RESEARCH INTO INTELLIGENT TRANSPORT SYSTEMS (ITS) TECHNOLOGIES AND EFFICIENCY

Transport ◽

10.3846/16484142.2007.9638100 ◽

2007 ◽

Vol 22 (2) ◽

pp. 61-67 ◽

Cited By ~ 26

Author(s):

Aldona Jarašūnienė

Keyword(s):

Human Factors ◽

Human Factor ◽

Early Stage ◽

Transport Systems ◽

Intelligent Transport Systems ◽

Intelligent Transport ◽

The Core ◽

Loop Detectors ◽

Control Technologies ◽

And Control

Intelligent Transport Systems work with information and control technologies which provide the core of ITS functions. Some of these technologies, like loop detectors, are well known to transportation professionals. However, there are a number of less familiar technologies and system concepts that are the key to ITS functions. The technical core of ITS is information and control technologies, but human factors are also vitally important, and potentially very complex. This paper introduces the main ITS enabling technologies and explains why transport professionals should involve human factor experts at an early stage of design of ITS equipment and facilities.

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Intelligent transport systems. Vehicle/roadway warning and control systems. Report on standardisation for vehicle automated driving systems (RoVAS)/Beyond driver assistance systems

10.3403/30319184 ◽

2017 ◽

Keyword(s):

Control Systems ◽

Transport Systems ◽

Driver Assistance ◽

Automated Driving ◽

Driver Assistance Systems ◽

Intelligent Transport Systems ◽

Intelligent Transport ◽

Assistance Systems ◽

And Control

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Intelligent transport systems - Data interfaces between centres for transport information and control systems - Platform independent model specifications for data exchange protocols for transport information and control systems

10.3403/30374962u ◽

2019 ◽

Keyword(s):

Control Systems ◽

Data Exchange ◽

Transport Systems ◽

Intelligent Transport Systems ◽

Intelligent Transport ◽

And Control ◽

Independent Model

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Cooperative Intelligent Transport Systems: Towards high-level automated driving

10.1049/pbtr025e ◽

2019 ◽

Keyword(s):

Transport Systems ◽

Automated Driving ◽

Intelligent Transport Systems ◽

Intelligent Transport ◽

High Level

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

Transport and Communication Science Journal ◽

10.25073/tcsj.70.3.7 ◽

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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