• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.3
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• pp.20-26
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• 2009

One way to increase public revenue from public parking spaces is by improving the way parking meters gather data. Improved parking meters have evolved that increase car park revenue as well as efficiency in service and operations. It provides parking meters with the capacity to provide real-time information. This information consist primarily of the status of parking spaces (it indicates which expired metered spaces have parked vehicles.) The technology consists of sensors located at meters that report on their condition (i.e. whether it is working or if it is expired). This information is processed by a microprocessor and then sent via an internal wireless modem to the server. This server then processes the information from all the parking meters and sends it to the public institution in charge of them. This system also provides verification of parking permits. For example, disabled people with special license plates can be automatically approved for parking in designated places.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.5
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• pp.730-737
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• 2016

Electric utilities may face new threats with increase in electric vehicles (EVs) in the personal automobile market. The peak demand will increase which may stress the distribution network equipment. The focus of this paper is on an adaptive control of smart household appliances by using an intelligent load management system (ILMS). The main objectives are to accomplish consumer needs and prevent overloading of power grid. The stress from the network is released by limiting the peak demand of a house when it exceeds a certain point. In the proposed strategy, for each smart appliance, the customers will set its order/rank according to their own preferences and then system will control the household loads intelligently for consumer reliability. The load order can be changed at any time by the customer. The difference between the set and actual value for each load's specific parameter will help the utility to estimate the acceptance of this intelligent load management system by the customers.

• Journal of Intelligence and Information Systems
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• v.16 no.1
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• pp.57-69
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• 2010

The ubiquitous transportation system environments make it possible to collect each vehicle's position and velocity data and to perform more sophisticated traffic flow management at individual vehicle or platoon level through V2V and V2I communication. It is necessary to develop a new data processing methodology to take advantage of the ubiquitous transportation system environments. This paper proposed to build 3-dimension data profiles to maintain the detailed traffic flow information contained in the individual vehicles' data and at the same time to keep the profiles from the meaningless fluctuations. Also methods to build the platoon profile and the shock wave speed profile are proposed, which have not been possible under ITS(Intelligent Transportation System) environments.

• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.31-39
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• 2006

This paper presents a novel approach to real-time recognition of 3D environment and objects for various applications such as intelligent robots, intelligent vehicles, intelligent buildings,..etc. First, we establish the three fundamental principles that humans use for recognizing and interacting with the environment. These principles have led to the development of an integrated approach to real-time 3D recognition and modeling, as follows: 1) It starts with a rapid but approximate characterization of the geometric configuration of workspace by identifying global plane features. 2) It quickly recognizes known objects in environment and replaces them by their models in database based on 3D registration. 3) It models the geometric details the geometric details on the fly adaptively to the need of the given task based on a multi-resolution octree representation. SIFT features with their 3D position data, referred to here as stereo-sis SIFT, are used extensively, together with point clouds, for fast extraction of global plane features, for fast recognition of objects, for fast registration of scenes, as well as for overcoming incomplete and noisy nature of point clouds.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.3
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• pp.86-99
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• 2021

This research proposes the Accident Prevention System to prevent collision accident that can occur due to blind spots such as crossway or school zone using V2I communication. Vision sensor and LiDAR sensor located in the infrastructure of crossway somewhere like that recognize objects and warn vehicles at risk of accidents to prevent accidents in advance. Using deep learning-based YOLOv4 to recognize the object entering the intersection and using the Manhattan Distance value with LiDAR sensors to calculate the expected collision time and the weight of braking distance and secure safe distance. V2I communication used ROS (Robot Operating System) communication to prevent accidents in advance by conveying various information to the vehicle, including class, distance, and speed of entry objects, in addition to collision warning.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.218-232
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• 2022

Researches are underway to use a radar sensor, a sensor used for object recognition in vehicles, for position estimation. In particular, a method of classifying dynamic and static objects using the Doppler velocity, the output from the radar sensor, and calculating ego-motion using only static objects has been researched recently. Also, for the existing dynamic object classification, several methods using RANSAC or robust filtering has been proposed. Still, a classification method with higher performance is needed due to the nature of the position estimation, in which even a single failure causes large effects. Hence, in this paper, we propose a method to improve the classification performance compared to existing methods through tracking and filtering of dynamic objects. Additionally, the method used a GMPHD filter to maximize tracking performance. In effect, the method showed higher performance in terms of classification accuracy compared to existing methods, and especially shows that the failure of the RANSAC could be prevented.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.208-218
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• 2023

Research on autonomous vehicles is being actively conducted, and the effort to commercialize them is underway in several countries. In Korea, platform construction projects are being carried out under the supervision of the Ministry of Land, Infrastructure, and Transport to achieve autonomous cooperative driving. To enable a flawless infrastructure, there is a requirement to build a safe security agency responsible for the secure operations of the entire process. However, there is no traffic ISAC in Korea that performs these roles. This paper analyzes related bills and acts of the other domestic security institutions currently in operation. Based on these results, we suggest appropriate directions to modify the current laws related to the C-ITS system. Finally, we propose a suitable plan to establish and operate a C-ITS ISAC.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.770-775
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• 2023

Recently, as the amount of digital data increases exponentially, the importance of data processing systems is being emphasized. In this situation, the selection and construction of data processing systems are becoming more important. In this study, the performance of cloud computing (CC), edge computing (EC), and UAV-based intelligent edge computing (UEC) was compared as a way to solve this problem. The characteristics, strengths, and weaknesses of each method were analyzed. In particular, this study focused on real-time large-capacity data processing situations such as corridor monitoring. When conducting the experiment, a specific scenario was assumed and a penalty was given to the infrastructure. In this way, it was possible to evaluate performance in real situations more accurately. In addition, the effectiveness and limitations of each computing method were more clearly understood, and through this, the help was provided to enable more effective system selection.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.1-8
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• 2024

On-device AI technology, which can operate AI models at the edge devices to support real-time processing and privacy enhancement, is attracting attention. As intelligent IoT is applied to various industries, services utilizing the on-device AI technology are increasing significantly. However, general deep learning models require a lot of computational resources for inference and learning. Therefore, various lightweighting methods such as quantization and pruning have been suggested to operate deep learning models in embedded edge devices. Among the lightweighting methods, we analyze how to lightweight and apply deep learning models to edge computing devices, focusing on pruning technology in this paper. In particular, we utilize dynamic and static pruning techniques to evaluate the inference speed, accuracy, and memory usage of a lightweight AI vision model. The content analyzed in this paper can be used for intelligent video control systems or video security systems in autonomous vehicles, where real-time processing are highly required. In addition, it is expected that the content can be used more effectively in various IoT services and industries.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.172-191
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• 2023

This study was undertaken to strategize the mixed traffic operation of autonomous vehicles in the pilot zone. This was achieved by analyzing the changes expected when autonomous vehicles are mixed in the autonomous vehicle pilot zone. Although finding a safe and efficient traffic operation strategy is required for the pilot zone to serve as a test bed for autonomous vehicles, there is no available operation strategy based on the mixture of autonomous vehicles. In order to presents a traffic operation strategies for each period of autonomous vehicle introduction, traffic efficiency and safety analysis was performed according to the autonomous vehicle market percentage rate. Based on the analysis results, the introduction stage was divided into introductory stage, transition period, and stable period based on the autonomous vehicle market share of 30% and 70%. This study presents the following traffic operation strategies. Considering the traffic flow operation strategy, we suggest the advancement of the existing road infrastructure at the introductory stage, and operating an autonomous driving lane and the mileage system during the transition period. We also propose expanding the operation of autonomous driving lanes and easing the speed limit during the stable period. In the traffic safety strategy, we present a manual and legal system for responding to autonomous vehicle accidents in the introductory stage, an analysis of the causes of autonomous vehicle accidents and the implementation of preventive policies in the transition period, and the advancement of the autonomous system and the reinforcement of the security system during the stable period. Through the traffic operation strategy presented in this study, we foresee the possibility of preemptively responding to the changes of traffic flow and traffic safety expected due to the mixture of autonomous vehicles in the autonomous vehicle pilot zone in the future.