• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.6
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• pp.810-820
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• 2014

Recently many studies of Radar systems mounted on ground vehicles for autonomous driving, SLAM (Simultaneous localization and mapping) and collision avoidance are reported. In near field, several hits per an object are generated after signal processing of Radar data. Hence, clustering is an essential technique to estimate their shapes and positions precisely. This paper proposes a method of grouping hits in range-doppler domains into clusters which represent each object, according to the pre-defined rules. The rules are based on the perceptual cues to separate hits by object. The morphological connectedness between hits and the characteristics of SNR distribution of hits are adopted as the perceptual cues for clustering. In various simulations for the performance assessment, the proposed method yielded more effective performance than other techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.3025-3047
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• 2021

This paper presents the latest Ethernet standardization of in-vehicle network and the future trends of automotive Ethernet technology. The proposed system provides design and optimization algorithms for automotive networking technology related to AVB (Audio Video Bridge) technology. We present a design of in-vehicle network system as well as the optimization of AVB for automotive. A proposal of Reduced Latency of Machine to Machine (RLMM) plays an outstanding role in reducing the latency among devices. RLMM's approach to real-world experimental cases indicates a reduction in latency of around 41.2%. The setup optimized for the automotive network environment is expected to significantly reduce the time in the development and design process. The results obtained in the study of image transmission latency are trustworthy because average values were collected over a long period of time. It is necessary to analyze a latency between multimedia devices within limited time which will be of considerable benefit to the industry. Furthermore, the proposed reliable camera and video streaming through optimized AVB device settings would provide a high level of support in the real-time comprehension and analysis of images with AI (Artificial Intelligence) algorithms in autonomous driving.

• Journal of Drive and Control
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• v.18 no.4
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• pp.9-18
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• 2021

This study proposes a method for creating learning datasets to recognize obstacles using deep learning algorithms in automated construction machinery or an autonomous vehicle. Recently, many researchers and engineers have developed various recognition algorithms based on deep learning following an increase in computing power. In particular, the image classification technology and image segmentation technology represent deep learning recognition algorithms. They are used to identify obstacles that interfere with the driving situation of an autonomous vehicle. Therefore, various organizations and companies have started distributing open datasets, but there is a remote possibility that they will perfectly match the user's desired environment. In this study, we created an interface of the virtual simulator such that users can easily create their desired training dataset. In addition, the customized dataset was further advanced by using the RDBMS system, and the recognition rate was improved.

• Journal of Information Technology Services
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• v.22 no.2
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• pp.57-70
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• 2023

In order to establish an autonomous driving environment, it is necessary to study traffic safety and demand prediction by analyzing information generated from the transportation infrastructure beyond relying on sensors by the vehicle itself. In this paper, we propose a real-time traffic information generation method using sensor convergence technology of transportation infrastructure. The proposed method uses sensors such as cameras and radars installed in the transportation infrastructure to generate information such as crosswalk pedestrian presence or absence, crosswalk pause judgment, distance to stop line, queue, head distance, and car distance according to each characteristic. create information An experiment was conducted by comparing the proposed method with the drone measurement result by establishing a demonstration environment. As a result of the experiment, it was confirmed that it was possible to recognize pedestrians at crosswalks and the judgment of a pause in front of a crosswalk, and most data such as distance to the stop line and queues showed more than 95% accuracy, so it was judged to be usable.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.171-176
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• 2023

What we have in common worldwide today is economic difficulties due to high inflation and uncertainty in the financial industry. The root cause of this is the war between Russia and Ukraine. The war between Russia and Ukraine is not simply a war between two countries. The United States and the European Union are providing military aid such as missiles to Ukraine, and Russia is attacking Ukraine by introducing UAVs (unmanned aerial vehicles) from Iran. A prominent weapon in this Russia-Ukraine war is the UAVs used in Russia. It is predicted that the form of war using UAVs will gradually expand in the future based on stealth. In addition, UAVs will continue to be used due to the fact that they can cause serious damage to the other country without harming their own lives, and because they have good cost-effectiveness. In this study, UAVs based on autonomous driving were studied. The target countries of the study include the United States, the European Union, China, and Iran, and the UAVs used in these countries have characteristics that can represent the world. In this study, the main specifications of major UAVs in use in major countries were investigated. In addition, the future technology and development direction were described through specifications and characteristics of UAVs currently in operation in major countries.

• Journal of Auto-vehicle Safety Association
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• v.10 no.2
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• pp.20-28
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• 2018

Currently, sharp increase of car is on the rise as a serious social problem due to loss of lives from car accident and environmental pollution. There is a study on ITS (Intelligent Transportation System) to seek coping measures. As for the commercialization of ITS, we aim for occupancy of world market through ASV (Advanced Safety Vehicle) related system development and international standardization. However, the domestic environment is very insufficient. Core factor technologies of ITS are Adaptive Cruise Control, Lane Keeping Assist System, Forward Collision Warning System, AEB (Autonomous Emergency Braking) system etc. These technologies are applied to cars to support driving of a driver. AEB system is stop the car automatically based on the result decided by the relative speed and distance with obstacle detected through sensor attached on car rather than depending on the driver. The purpose of AEB system is to measure the distance and speed of car and to prevent accident. Thus, AEB will be a system useful for prevention of accident by decreasing car accident along with the development of automobile technology. This study suggests a scenario to suggest a test evaluation method that accords with domestic environment and active response of international standard regarding the test evaluation method of AEB. Also, by setting the goal with function for distance, it suggests theoretic model according to the result. And the study aims to verify the theoretic evaluation standard per proposed scenario using car which is installed with AEB device through field car driving test on test road. It will be useful to utilize the suggested scenario and theoretical model when conducting AEB test evaluation.

• Electronics and Telecommunications Trends
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• v.39 no.3
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• pp.69-78
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• 2024

Cloud computing technology based on centralized high-performance computing has brought about major changes across the information technology industry and led to new paradigms. However, with the rapid development of the industry and increasing need for mass generation and real-time processing of data across various fields, centralized cloud computing is lagging behind the demand. This is particularly critical in emerging technologies such as autonomous driving, the metaverse, and augmented/virtual reality that require the provision of services with ultralow latency for real-time performance. To address existing limitations, distributed and edge cloud computing technologies have recently gained attention. These technologies allow for data to be processed and analyzed closer to their point of generation, substantially reducing the response times and optimizing the network bandwidth usage. We describe distributed and edge cloud computing technologies and explore the latest trends in their standardization.

• Korean Journal of Construction Engineering and Management
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• v.23 no.4
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• pp.3-14
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• 2022

Autonomous equipment requires a large amount of data from various environments. However, it takes a lot of time and cost for an experiment in a real construction sites, which are difficulties in data collection and processing. Therefore, this study aims to develop a virtual environment for autonomous tower cranes technology development and validation. The authors defined automation functions and operation conditions of tower cranes with three performance criteria: operational design domain, object and event detection and response, and minimum functional conditions. Afterward, this study developed a virtual environment for learning and validation for autonomous functions such as recognition, decision making, and control using the Unity game engine. Validation was conducted by construction industry experts with a fidelity which is the representative matrix for virtual environment assessment. Through the virtual environment platform developed in this study, it will be possible to reduce the cost and time for data collection and technology development. Also, it is also expected to contribute to autonomous driving for not only tower cranes but also other construction equipment.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.179-186
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• 2015

In this paper, a novel lateral control system is proposed for the purpose of improving lane keeping performance which is independent from GPS signals. Lane keeping is a key function for the realization of unmanned driving systems. In order to obtain this objective, a vision sensor based real-time lane detection scheme is developed. Furthermore, we employ a data fusion along with a real-time steering angle of the test vehicle to improve its lane keeping performance. The fused direction data can be obtained by an IMU sensor and vision sensor. The performance of the proposed system was verified by computer simulations along with field tests using MOHAVE, a commercial vehicle from Kia Motors of Korea.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.5
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• pp.377-388
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• 2020

In this paper, the autonomous mobile robot whit only LRF sensors proposes an algorithm for avoiding moving obstacles in an environment where a global map containing fixed obstacles. First of all, in oder to avoid moving obstacles, moving obstacles are extracted using LRF distance sensor data and a global map. An ellipse-shaped safety radius is created using the sum of relative vector components between the extracted moving obstacles and of the autonomuos mobile robot. Considering the created safety radius, the autonomous mobile robot can avoid moving obstacles and reach the destination. To verify the proposed algorithm, use quantitative analysis methods to compare and analyze with existing algorithms. The analysis method compares the length and run time of the proposed algorithm with the length of the path of the existing algorithm based on the absence of a moving obstacle. The proposed algorithm can be avoided by taking into account the relative speed and direction of the moving obstacle, so both the route and the driving time show higher performance than the existing algorithm.