• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.123-133
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• 2024

Existing autonomous driving technology has been developed based on sensors attached to the vehicles to detect the environment and formulate driving plans. On the other hand, it has limitations, such as performance degradation in specific situations like adverse weather conditions, backlighting, and obstruction-induced occlusion. To address these issues, cooperative autonomous driving technology, which extends the perception range of autonomous vehicles through the support of road infrastructure, has attracted attention. Nevertheless, the real-time analysis of the 3D centroids of objects, as required by international standards, is challenging using single-lens cameras. This paper proposes an approach to detect objects and estimate the centroid of vehicles using the fixed field of view of road infrastructure and pre-measured geometric information in real-time. The proposed method has been confirmed to effectively estimate the center point of objects using GPS positioning equipment, and it is expected to contribute to the proliferation and adoption of cooperative autonomous driving infrastructure technology, applicable to both vehicles and road infrastructure.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.2
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• pp.110-116
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• 2016

Autonomous vehicles must obey the traffic laws in order to drive actual roads. Traffic signs erected at the side of roads explain the road traffic information or regulations. Therefore, traffic sign recognition is necessary for the autonomous vehicles. In this paper, color characteristics are first considered to detect traffic sign candidates. Subsequently, we establish HOG (Histogram of Oriented Gradients) features from the detected candidate and recognize the traffic sign through a SVM (Support Vector Machine). However, owing to various circumstances, such as changes in weather and lighting, it is difficult to recognize the traffic signs robustly using only SVM. In order to solve this problem, we propose a tracking algorithm with RANSAC-based motion estimation. Using two-point motion estimation, inlier feature points within the traffic sign are selected and then the optimal motion is calculated with the inliers through a bundle adjustment. This approach greatly enhances the traffic sign recognition performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.154-160
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• 2012

Recently, development of technologies for autonomous vehicles has been actively carried out. This paper proposes a computer vision system to recognize lanes, crosswalks, and stop lines for autonomous vehicles. This vision system first recognizes lanes required for autonomous driving using the RANSAC algorithm and the Kalman filter, and changes the viewpoint from the perspective-angle view of the street to the top-view using the fact that the lanes are parallel. Then in the reconstructed top-view image this system recognizes a crosswalk based on its geometrical characteristics and searches for a stop line within a region of interest in front of the recognized crosswalk. Experimental results show excellent performance of the proposed vision system in recognizing lanes, crosswalks, and stop lines.

• Journal of Auto-vehicle Safety Association
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• v.12 no.2
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• pp.27-32
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• 2020

This paper presents steering system requirements to ensure the stabilized lateral control of autonomous driving vehicles. The two main objectives of a lateral controller in autonomous vehicles are maintenance of vehicle stability and tracking of the desired path. Even if the desired steering angle is immediately determined by the upper level controller, the overall controller performance is greatly influenced by the specification of steering system actuators. Since one of the major inescapable traits that affects controller performance is the time delay of the steering actuator, our work is mainly focused on finding adequate parameters of high level control algorithm to compensate these response characteristics and guarantee vehicle stability. Actual vehicle steering angle response was obtained with Electric Power Steering (EPS) actuator test subject to various longitudinal velocity. Steering input and output response analysis was performed via MATLAB system identification toolbox. The use of system identification is advantageous since the transfer function of the system is conveniently obtained compared with methods that require actual mathematical modeling of the system. Simulation results of full vehicle model suggest that the obtained tuning parameter yields reduced oscillation and lateral error compared with other cases, thus enhancing path tracking performance.

• The Journal of Information Systems
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• v.30 no.4
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• pp.95-118
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• 2021

Purpose This study verified the moderating effect of consumer knowledge in relation to the factors affecting the acceptance intention of autonomous vehicles by adding trust to the United Theory of Acceptance and Use of Technology model for the commercialization of autonomous vehicles. Design/methodology/approach For this purpose, this study conducted a survey on general consumers who are interested in automobiles. A total of 250 questionnaires were distributed and collected, and 242 questionnaires were used for analysis. To test the hypotheses, multiple regression analysis and multiple group analysis were performed. Findings Performance expectations, effort expectations, social influence, and trust were found to have a positive effect on the acceptance intention of autonomous vehicles. In addition, consumer knowledge between performance expectation and acceptance intention and between effort expectation and acceptance intention was confirmed as a variable that can moderate the relationship.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.47-54
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• 2022

For safe driving of autonomous vehicles, road damage detection is very important to lower the potential risk. In order to ensure safety while an autonomous vehicle is driving on the road, technology that can cope with various obstacles is required. Among them, technology that recognizes static obstacles such as poor road conditions as well as dynamic obstacles that may be encountered while driving, such as crosswalks, manholes, hollows, and speed bumps, is a priority. In this paper, we propose a method to extract similarity of images and find damaged road images using OpenCV image processing and CNN algorithm. To implement this, we trained a CNN model using 280 training datasheets and 70 test datasheets out of 350 image data. As a result of training, the object recognition processing speed and recognition speed of 100 images were tested, and the average processing speed was 45.9 ms, the average recognition speed was 66.78 ms, and the average object accuracy was 92%. In the future, it is expected that the driving safety of autonomous vehicles will be improved by using technology that detects road obstacles encountered while driving.

• Journal of Auto-vehicle Safety Association
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• v.15 no.3
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• pp.53-58
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• 2023

Defects in the vehicle itself were considered the biggest risk factor for traffic accidents as the electrical and electronic components of vehicles, which were not there before, increase. Therefore, the vehicles have been developed based on ISO 26262 (an international functional safety standard) which is focusing on functional defect safety evaluation of electrical and electronic component systems. However, in the future, as autonomous driving technology is applied, even vehicles without functional defects must be prepared for the dangerous traffic situation that may arise from exceptional or external factors. SOTIF (Safety Of The Intended Functionality) is a concept to prevent exceptional or external factors. The main objective of SOTIF is to decrease Unknown & Unsafe factors as much as possible by finding Known factors and Unsafe factors. In this study, Prescan provided SIEMENS, one of the autonomous driving simulators, is used to make scenarios of IGLAD traffic accident cases. From the simulation results, Unsafe & Safe cases were classified and analyzed to derive unsafe factors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.251-257
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• 2022

Self-driving vehicles are a type of smart vehicle with the help of ICT technology, which means a vehicle that operates without the intervention of a driver.Vehicles with vehicle safety communication technology (V2X) applied use information detected from various sensors or other vehicles/infrastructures to enable the smart vehicle to accurately and quickly predict the driver's potential danger situation, contributing to more stable autonomous driving. In this paper, among V2X communication technologies, a vehicle-to-vehicle communication (V2V) simulation communication technology is used to present a scenario for preventing collisions in autonomous vehicles. A vehicle collision prevention system based on V2V simulated communication was implemented and the suggested collision prevention application scenario was demonstrated. The suggested collision prevention utilization scenario can be considered as one application case of V2V communication technologies that are currently being developed/applied.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.5
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• pp.760-767
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• 2011

To strategically select the technology priority based on the understanding of technology development trends and prospects is very important. To provide such guidance for autonomous driving technology in unmanned ground combat vehicles, this report deals with followings; 1) The core technologies for autonomous driving are reviewed. 2) And domestic and foreign policies for relevant technology development are investigated. 3) Then, to estimate the technology development trend, the published patents and research papers are analyzed. 4) Based on those analyses, domestic technology level and development prospects are expected.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.11a
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• pp.230-232
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• 2019

There is an accelerating need for ocean sensing where autonomous vehicles can play a key role in assisting engineers, researcher and scientists with environmental monitoring and collecting oceanographic data. This paper is performed to develops an autonomous sailing drone to be used as a sensor carrying platform for autonomous data acquisition at Sea. From a sailing drone design viewpoint, it is important to establish reliable prediction methods for sailing drone's resistance. The required power for the propulsion unit depends on the ship resistance and speed. There are three solutions for the prediction of ship resistance as follow analytical methods, model tests in tanks and Computational Fluid Dynamics (CFD). The present paper aims at simulating sailing drone friction resistance using numerical method. The dynamic mesh motion is used to describe the sailing drone movement.