• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.8
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• pp.123-132
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• 2017

Traffic lights contain very important information for safety driving. So, the delivery of the information to drivers in real-time is a very critical issue for advanced driver assistance systems. However, traffic light detection is quite difficult because of the small sized traffic lights and the occlusion in real world. In this paper, a traffic light detection method using modified color based saliency map and morphological information is proposed. It shows 98.14% of precisions and 83.52% of recalls on computer simulations.

• Journal of Broadcast Engineering
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• v.22 no.1
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• pp.62-69
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• 2017

Accurate detection of traffic lights is very important for the advanced driver assistance system (ADAS). There have been many research developments in this area. However, conventional of image processing methods are usually sensitive to varying illumination conditions. This paper proposes a traffic light detection algorithm to overcome this situation. The proposed algorithm first detects the candidates of traffic light using the proposed color map and hue-saturation-value (HSV) Traffic lights are then detected using the conventional histogram of oriented gradients (HOG) descriptor and support vector machine (SVM). Finally, the proposed Multilayer HOG descriptor is used to determine the direction information indicated by traffic lights. The proposed algorithm shows a high detection rate in real-time.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.85-90
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• 2023

The Ant Colony System (ACS) is a variant of Ant colony optimization algorithm which is well-known in Traveling Salesman Problem. This paper proposed a hybrid method based on genetic algorithm (GA) and ant colony system (ACS), called GACS, to solve traffic routing problem. In the GACS, we use genetic algorithm to optimize the ACS parameters that aims to attain the shortest trips and time through new functions to help the ants to update global and local pheromones. Our experiments are performed by the GACS framework which is developed from VANETsim with the ability of real map loading from open street map project, and updating traffic light in real-time. The obtained results show that our framework acquired higher performance than A-Star and classical ACS algorithms in terms of length of the best global tour and the time for trip.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.5
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• pp.49-58
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• 2010

The traffic signal controllers being used in the domestic currently are being manufactured based on the korean national police standard which was developed for controlling the quad-light traffic signal having the red, yellow, left-turn arrow, and green lights. But according to the national policy for the traffic operation, they have to be changed to be able to switch the tri-light signal having red, yellow and green lights. In this study, a new tri-light traffic signal controller was designed and developed by the way improving the Signal Control Unit of the existing quad-light standard traffic controller. The Load Signal Unit(LSU) was improved to output 6 signals which are the two assemblies of three signal indications having the red, yellow, and green lights. To enough traffic signals output to control each directional movements and the various transport modes which are car, bus, bike, and pedestrian etc., the connector bus system was designed to be able to accommodate maximum 96 signals outputs being constructed by 16 LSUs. Flasher device was developed to be able to support maximum 32 red signals. In the software, the communication protocol between traffic control center and the traffic signal controller was improved and new signal map code values were defined for the developed LSU controlling the quad-light traffic signal. A model of the quad-light traffic signal controller developed and was tested three operations, protocol-operation, remote-command and control-mode. The test result operated all of them successfully.

• 대한예방의학회:학술대회논문집
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• 2005.10a
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• pp.495-495
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• 2005

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.137-149
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• 2019

Traffic emissions are the main cause of environmental pollution in cities and respiratory problems amongst people. This study developed a model based on an integration of support vector regression (SVR) algorithm and geographic information system (GIS) to map traffic carbon monoxide (CO) concentrations and produce prediction maps from micro level to macro level at a particular time gap in a day in a very densely populated area (Utara-Selatan Expressway-NKVE, Kuala Lumpur, Malaysia). The proposed model comprised two models: the first model was implemented to estimate traffic CO concentrations using the SVR model, and the second model was applied to create prediction maps at different times a day using the GIS approach. The parameters for analysis were collected from field survey and remote sensing data sources such as very-high-resolution aerial photos and light detection and ranging point clouds. The correlation coefficient was 0.97, the mean absolute error was 1.401 ppm and the root mean square error was 2.45 ppm. The proposed models can be effectively implemented as decision-making tools to find a suitable solution for mitigating traffic jams near tollgates, highways and road networks.

• Journal of Auto-vehicle Safety Association
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• v.9 no.3
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• pp.24-30
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• 2017

This paper presents the development of laser scanner based static obstacle detection algorithm for vehicle localization on lane lost section. On urban autonomous driving, vehicle localization is based on lane information, GPS and digital map is required to ensure. However, in actual urban roads, the lane data may not come in due to traffic jams, intersections, weather conditions, faint lanes and so on. For lane lost section, lane based localization is limited or impossible. The proposed algorithm is designed to determine the lane existence by using reliability of front vision data and can be utilized on lane lost section. For the localization, the laser scanner is used to distinguish the static object through estimation and fusion process based on the speed information on radar data. Then, the laser scanner data are clustered to determine if the object is a static obstacle such as a fence, pole, curb and traffic light. The road boundary is extracted and localization is performed to determine the location of the ego vehicle by comparing with digital map by detection algorithm. It is shown that the localization using the proposed algorithm can contribute effectively to safe autonomous driving.

• Journal of the Korean housing association
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• v.25 no.2
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• pp.63-70
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• 2014

The dependence on cars for urban mobility and the exponential increase in traffic and urban infrastructure to sustain traffic have lead to an encapsulated way of life, where the connection with the natural environment is much more reduced and programmed. In a previous study, a process based on estimating distances showed that children who move around their city by automobile do not appreciate their environment as a spatial continuum, but rather as a series of independent spaces that are reached by automobile or bus, thereby evidencing a different way of conceptualizing urban space in the light of different cognitive structures (Goluboff, Garc$\acute{i}$a-Mira, and Garc$\acute{i}$a-Font$\acute{a}$n, 2002). The present study is concerned with the process of understanding and knowledge of urban space, and contrasting the cognitive structure of different groups. The implications that this study may have for urban planning are discussed.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.306-308
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• 2016

신호등 검출은 지능형 교통 시스템에서 매우 중요하며 최근 신호등 검출 관련한 연구가 활발히 진행 중이다. 하지만 기존의 신호등검출 알고리듬의 문제점은 조명의 변화에 민감하다는 문제점이 있다. 이러한 문제점을 해결하기 위하여 본 논문에서는 다음과 같은 신호등 검출 알고리듬을 제안한다. 먼저 제안하는 색상지도와 HSV(Hue-Saturation-Value)를 이용하여 신호등의 후보를 검출한다. 검출한 신호등의 후보로부터 HOG(Histogram of Oriented Gradient) 서술자를 이용하여 특징을 추출한 다음 최종적으로 선형 SVM(Support Vector Machine)을 이용하여 신호등을 검출하는 알고리듬을 제안한다.

• Journal of Cadastre & Land InformatiX
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• v.53 no.2
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• pp.53-66
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• 2023

Recently, there has been a significant interest in the development of autonomous driving simulation environment based on digital twin. In the development of such digital twin-based simulation environment, many researches has been conducted not only performance and functionality validation of autonomous driving, but also generation of virtual training data for deep learning. However, such digital twin-based autonomous driving simulation system has the problem of requiring a significant amount of time and cost for the system development and the data construction. Therefore, in this research, we aim to propose a method for rapidly designing and implementing a digital twin-based autonomous driving simulation system, using only the existing 3D models and high-definition map. Specifically, we propose a method for integrating 3D model of FBX and NGII HD Map for the Busan EDC area into CARLA, and a method for adding and modifying CARLA functions. The results of this research show that it is possible to rapidly design and implement the simulation system at a low cost by using the existing 3D models and NGII HD map. Also, the results show that our system can support various functions such as simulation scenario configuration, user-defined driving, and real-time simulation of traffic light states. We expect that usability of the system will be significantly improved when it is applied to broader geographical area in the future.