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

Intelligent transport systems (ITS) are a promising area of studies. One implementation of ITS are advanced driver assistance systems (ADAS), involving the problem of obstacle detection in traffic. This study evaluated the YOLOv4 model as a state-of-the-art CNN-based one-stage detector to recognize traffic obstacles. A new dataset is proposed containing traffic obstacles on Indonesian roads for ADAS to detect traffic obstacles that are unique to Indonesia, such as pedicabs, street vendors, and bus shelters, and are not included in existing datasets. This study established a traffic obstacle dataset containing eleven object classes: cars, buses, trucks, bicycles, motorcycles, pedestrians, pedicabs, trees, bus shelters, traffic signs, and street vendors, with 26,016 labeled instances in 7,789 images. A performance analysis of traffic obstacle detection on Indonesian roads using the dataset created in this study was conducted using the YOLOv4 method.

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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

10.3403/30374962 ◽

2019 ◽

Keyword(s):

Control Systems ◽

Data Exchange ◽

Transport Systems ◽

Intelligent Transport Systems ◽

Intelligent Transport ◽

And Control ◽

Independent Model

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Pedestrian Crossings Detection by using Driving Assistance Systems

Journal of Communications Technology Electronics and Computer Science ◽

10.22385/jctecs.v9i0.127 ◽

2017 ◽

Vol 9 ◽

pp. 17

Author(s):

Takialddin Al Smadi

Keyword(s):

Detection System ◽

Transport Systems ◽

Intelligent Transport Systems ◽

Intelligent Transport ◽

Histograms Of Oriented Gradients ◽

Driving Assistance ◽

Detection Of Objects ◽

Assistance Systems ◽

And Performance ◽

And Control

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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