• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.4
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• pp.145-156
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• 2022

To strengthen the security of autonomous vehicles, this study derived checklists through the analysis of the status of autonomous vehicle security. The analyzed statuses include autonomous vehicle characteristics, security threats, and domestic and foreign security standards. The derived checklists are then applied to the AHP(Analytic Hierarchy Process) model to find their relative importance. Relative importance was ranked as one of cyber security management system establishment and implementation, encryption, risk assessment, etc. The significance of this study is to reduce cyber security incidents that cause human casualties as well improve the level of security management of autonomous vehicles in related companies by deriving the autonomous vehicle security level checklists and demonstrating the model. If the inspection is performed considering the relative importance of the checklists, the security level can be identified early.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.67-77
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• 2022

An automatic freeway bus-only lane enforcement system was developed and assessed in a real-world environment. Observation of a bus-only lane on the Youngdong freeway, South Korea, revealed that approximately 99% of the vehicles violated the high-occupancy vehicle (HOV) lane regulation. However, the current enforcement by the police not only exhibits a low enforcement rate, but also induces unnecessary safety and delay concerns. Since vehicles with six passengers or higher are permitted to enter freeway bus-only lanes, identifying the number of passengers in a vehicle is a core technology required for a freeway bus-only lane enforcement system. To that end, infrared cameras and the You Only Look Once (YOLOv5) deep learning algorithm were utilized. For assessment of the performance of the developed system, two environments, including a controlled test-bed and a real-world freeway, were used. As a result, the performances under the test-bed and the real-world environments exhibited 7% and 8% errors, respectively, indicating satisfactory outcomes. The developed system would contribute to an efficient freeway bus-only lane operations as well as eliminate safety and delay concerns caused by the current manual enforcement procedures.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.147-163
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• 2023

Recently, there has been an increase in the research and development of automated vehicles worldwide. Research focused on automated vehicles in Korea is steadily progressing as a national R&D project. Since automated driving technology comprises diverse technology fields, it is necessary to identify the current position of the research. In this study, we propose a methodology for analyzing research trends using the NTIS data. In addition, we review the effectiveness of the currently developed research trend methodology by deriving primary keywords and major topics using the proposed method. We expect that the methodology developed in this study can be applied to identify and analyze future automated vehicle research trends.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.291-310
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• 2023

This study explores the factors which affect user acceptance for Robo Taxi, an electricity-based Autonomous Vehicles based on a Value based Adoption Model. The three main factors of benefit (usefulness and enjoyment), sacrifice (technicality and perceived fee level), and user experience about mobility services such as car sharing, taxi, and autonomous vehicles, were finally selected as independent variables as a influential factors on perceived values and adoption intention of Robo taxi. The study found that usefulness, enjoyment, and perceived fee had a significant effects on adoption intention, and some user experiences had a significant effect on benefit factors. This study has important implications for incorporating the Value-based Adoption Model results into the service design for the activation of Robo taxi, and furthermore, they can provide a theoretical basis for effective use of the research findings.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.175-189
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• 2023

Fault detection and diagnosis (FDI) algorithms are actively being researched for ensuring the integrity and reliability of environment perception sensors in autonomous vehicles. In this paper, a fault detection algorithm based on a multi-sensor perception system composed of radar, camera, and lidar is proposed to guarantee the safety of an autonomous vehicle's perception system. The algorithm utilizes reference generation filters and residual generation filters based on finite impulse response (FIR) filter estimates. By analyzing the residuals generated from the filtered sensor observations and the estimated state errors of individual objects, the algorithm detects faults in the environment perception sensors. The proposed algorithm was evaluated by comparing its performance with a Kalman filter-based algorithm through numerical simulations in a virtual environment. This research could help to ensure the safety and reliability of autonomous vehicles and to enhance the integrity of their environment perception sensors.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.92-107
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• 2023

Recently, along with the continuous increase in the supply of electric vehicles, electric vehicle fire accidents are also showing a rapidly increasing trend. Electric vehicle fires last for a long time compared to fires in internal combustion engine vehicles and have problems with the risk of secondary explosions and the generation of large amounts of smoke. In particular, electric vehicle fires in underground roads, which are semi-enclosed spaces, may amplify the problems of existing electric vehicle fires. On the other hand, there are no domestic response guidelines for electric vehicle fires occurring inside underground roads. Therefore, an awareness of fire accidents was confirmed through a survey of the general public, and electric vehicle fire characteristics and primary considerations were derived from stakeholders related to electric vehicle fires in underpasses. Through this, the guidelines for responding to electric vehicle fires on underground roads were established.

• Journal of Internet of Things and Convergence
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• v.8 no.2
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• pp.35-40
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• 2022

In the agricultural environment, pesticide control requires a high risk of work and a high labor force for farmers. The effectiveness of pesticide control using unmanned aerial vehicles varies according to climate, land type, and characteristics of unmanned aerial vehicles. Therefore, an effective method for pesticide control by unmanned aerial vehicles considering the spraying conditions and environmental conditions is required. In this paper, we propose an efficient pesticide control system based on agricultural unmanned aerial vehicles considering the application conditions and environmental information for each crop. The effectiveness of the proposed model was demonstrated by measuring the drop uniformity of pesticides according to the change in altitude and speed after attaching the sensory paper and measuring the penetration rate of the drug inside the canopy according to the change in crop growth conditions. Experiment result, the closer the height of the UAV is to the ground, the more evenly the crops are sprayed, but for safety reasons, 2m more is suitable, and on average a speed of 2m/s is most suitable for control. The proposed control system is expected to help develop intelligent services based on the use of various unmanned aerial vehicles in agricultural environments.

• Korean Journal of Cognitive Science
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• v.25 no.1
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• pp.51-71
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• 2014

This paper presents an image enhancement method in real road traffic scenes. The images captured by the camera on the car cannot keep the color constancy as illumination or weather changes. In the real environment, these problems are more worse at back light conditions and at night that make more difficult to the applications of the vision-based intelligent vehicles. Using the existing image enhancement methods without considering the position and intensity of the light source and their geometric relations the image quality can even be deteriorated. Thus, this paper presents a fast and effective method for image enhancement resembling human cognitive system which consists of 1) image preprocessing, 2) color-contrast evaluation, 3) alpha blending of over/under estimated image and preprocessed image. An input image is first preprocessed by gamma correction, and then enhanced by an Automatic Color Enhancement(ACE) method. Finally, the preprocessed image and the ACE image are blended to improve image visibility. The proposed method shows drastically enhanced results visually, and improves the performance in traffic sign detection of the vision based intelligent vehicle applications.

• Journal of Digital Convergence
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• v.16 no.4
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• pp.87-94
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• 2018

Intelligent transportation systems are one of the most important new forms of infrastructure on domestic roads, and is a system that makes possible the most efficient movement of vehicles on a road. The High Pass system, which is a domestic intelligent transportation system, started a little later than in other countries but developed at a rapid pace. With the recent introduction of smart tolling technology, it provided an opportunity to stop and review the tolling system. This study aims to investigate the driving method and results of LDCC for domestic smart towing through case study. Unlike other companies, Lotte Data Communication Company has long invested in payment systems. It has little experience investing in infrastructure, but participated in the Smart Toll System at the Gwangan Bridge in cooperation with the Busan City government, to lead the development of intelligent transportation systems. LDCC, which has made new investments, not only exceeded its existing core competencies, but also upgraded Korea's tolling system's ability to reduce greenhouse gas emissions and improved its financial performance.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.1
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• pp.9-21
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• 2009

This study presents a useful heuristic algorithm to classify vehicle classes using vehicle length information, which is extracted from inductive loop vehicle signatures. A high-speed scanning equipment was used to extract more detailed change of inductance magnitude for individual vehicles. Vehicle detection time and individual vehicle speeds were used to derive vehicle length information that is an input of the proposed algorithm. The spatial and temporal transferability tests were further conducted to evaluate algorithm. The spatial and temporal transferability tests were further conducted to evaluate algorithm performance more systematically. It is expected that the proposed method would be useful for obtaining vehicle classification information from wide-spread existing loop infrastructure.