• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.2
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• pp.50-57
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• 2023

Autonomous driving technologies have been gradually improved for road traffic owing to the development of artificial intelligence. Since the truck platooning is beneficial in terms of the associated transporting expenses, the Connected-Automated Vehicle technology is rapidly evolving. The structural performance is, however, rarely investigated to capture the effect of truck platooning on civil infrastructures.In this study, the dynamic behavior of bridges under truck platooning was investigated, and the amplification factor of responses was estimated considering several parameters associated with the driving conditions. Artificial intelligence techniques were used to estimate the maximum response of the mid span of a bridge as the platooning vehicles passing, and the importance of the parameters was evaluated. The most suitable algorithm was selected by evaluating the consistency of the estimated displacement.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.97-114
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• 2022

Unlike ground roads, the on-ramp merging section of underground roads cannot be seen by drivers of main road due to tunnels. In this study, a driving simulator was used for analysis, and virtual driving experiments were carried out to assess the driver's visibility for different design factors. The driver's driving behavior was analyzed by setting scenarios considering the length of chevron markings and the approach speed from the main road. The results of the analysis were used to determine the design factors for ensuring visibility when constructing the virtual driving environment for each scenario. These factors, including speed, lane change points, and driver's gaze ratios, were reviewed for significance using a statistical approach. As a result, in scenarios with a higher approach speed from the main road, it was discovered that there was a difference in driver's behavior between specific scenarios depending on the length of the section with chevron markings. Based on these findings, this study suggests implications and feasible solutions to improve driver safety on the on-ramp merging section of underground roads.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2017.05a
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• pp.489-497
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• 2017

사물 인터넷, 클라우드 컴퓨팅, 인공지능, 빅데이터 등 첨단 정보통신기술을 활용하여 모든 사물들의 지능화와 초연결을 지향하는 것을 나타내는 4차 산업혁명에 대한 관심은 나날이 증가하고 있다. 4차 산업혁명과 관련된 다양한 분야의 기술들 중에서 인공지능을 활용한 자율주행자동차 기술과 관련하여 논의를 진행하고자 한다. 여러 국가에서는 자율주행자동차 시장선점을 위해 관련 기술의 발달에 몰두하고 있다. 그러나 자율주행자동차의 상용화를 위해서는 기술의 발달뿐만 아니라 관련 법제도의 정비가 더욱 필수적인 요소라고 본다. 자율주행자동차가 안전성을 보장할 수 있도록 시험운행에 대한 규제를 완화시켜야 할 것이며, 향후 상용화가 될 경우 야기될 수 있는 민사적 손해배상문제와 형사책임의 문제, 과실책임의 문제, 사이버 보안문제 등을 대비할 수 있는 법제도 마련을 촉구하고자 한다. 타국가와의 자율주행자동차 법제도 비교분석을 통해 우리나라의 도로교통법과 기존 법 제정상태를 고려하여 법안 마련에 방향성을 설정하는 데에 목표를 두고 있다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.28-41
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• 2022

A road system with CV(Connected Vehicle)s, which is often referred to as a cooperative intelligent transportation system (C-ITS), provides various road information to drivers using an in-vehicle warning system. Road environments with CVs induce drivers to reduce their speed or change lanes to avoid potential risks downstream. Such avoidance maneuvers can be considered to improve driving behaviors from a traffic safety point of view. Thus, empirically evaluating how a given in-vehicle warning information affects driving behaviors, and monitoring of the correlation between them are essential tasks for traffic operators. To quantitatively evaluate the effect of in-vehicle warning information, this study develops a method to calculate compliance rate of drivers where two groups of speed profile before and after road information is provided are compared. In addition, conventional indexes (e.g., jerk and acceleration noise) to measure comfort of passengers are examined. Empirical tests are conducted by using PVD (Probe Vehicle Data) and DTG (Digital Tacho Graph) data to verify the individual effects of warning information based on C-ITS constructed in Seoul metropolitan area in South Korea. The results in this study shows that drivers tend to decelerate their speed as a response to the in-vehicle warning information. Meanwhile, the in-vehicle warning information helps drivers to improve the safety and comport of passengers.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.4
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• pp.13-27
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• 2021

In this study, the impact of traffic flow on the market penetration rate of autonomous vehicles(AV) was analyzed using the data for the year 2020 of the Yongin IC~Yangji IC section of Yeongdong Expressway. For this analysis, a microscopic traffic simulation model VISSIM was utilized. To construct the longitudinal control of the AV, the Intelligent Driver Model(IDM) was built and applied, and the driving behavior was verified by comparison with a normal vehicle. An examination of the study results of mobility and safety according to the market penetration rate of the AV, showed that the network's mobility improves as the market penetration rate increases. However, from the point of view of safety, the network becomes unstable when normal vehicles and AVs are mixed, so there should be a focus on traffic management for ensuring safety in mixed traffic situations.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.87-88
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• 2019

최근 기계학습 기술의 급속한 발전에 힘입어 자율주행을 위한 객체 인식 및 처리 기술 역시 비약적으로 발전하고 있다. 그러나 이러한 기계학습의 성능은 모델의 구조와 학습용 데이터의 품질에 영향을 받는다. 특히 주행환경을 잘 표현하는 학습데이터가 중요한데 전혀 새로운 도로, 주행환경, 장애물, 정적 혹은 동적 객체 등을 마주하면 정확도와 안정성에서 부정적인 영향을 받을 수 있는 것이다. 해외의 주행 데이터들에 크게 의존하고 있는 우리나라의 현실에 비춰 볼 때 국내 환경에 맞는 학습데이터를 쉽고 효율적으로 확보/관리/분석할 수 있게 하는 환경의 구축이 시급하다. 따라서 본 논문에서는 자율주행을 위한 기계학습 데이터를 효과적으로 관리하고 분석하기 위한 소프트웨어를 설계하고 개발하였다. 구체적으로는 수집된 영상들을 관리하는 기능, 영상에 존재하는 노이즈 제거 및 화질 개선 처리 기능, 학습 및 검증을 위한 메타 정보 태깅 기능, 태깅 정보의 통계적 분석 기능들을 포함한다. 개발한 소프트웨어는 우리나라에서 자체 촬영한 자율주행 학습 영상들에 대해 딥러닝 모델들을 학습하고 검증하는데 활용할 예정이다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.7
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• pp.599-605
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• 2017

For an unmanned vehicle to be driven on the off-road terrain, it is necessary to consider the vehicle's stability. This paper suggests a path tracking controller for simulation of real-time vehicle stability analysis. The path tracking controller uses the preview distance to track the given trajectory. The disturbance moment is estimated using the yaw moment observer, and this information is used for compensation in the yaw moment control. On a curved path, the vehicle's desired velocity is determined from the curvature of the path. Because the vehicle is equipped with six independent motor driven wheels, the driving torques are distributed on all the wheels. The effectiveness of the path tracking controller is verified using ADAMS/MATLAB co-simulation.

• Journal of the Korean Society for Railway
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• v.16 no.2
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• pp.110-116
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• 2013

Track irregularities is one of the key factors influencing the running behavior of trains. In order to ensure safety and ride comfort of train, the criteria for track irregularities should be adequately established regarding vehicle velocity, vehicle characteristics, characteristics of the track recording car, and measurement interval. Also, track maintenance should be carried out thoroughly according to the criteria for managing track irregularities. Numerical analysis was conducted to investigate the influence of track alignment on the running behavior of Korean high speed train(KTX). Various wavelengths and amplitudes of lateral alignment were considered as parameters for this study using the Vampire program, a vehicle dynamics modeling package in railway environment. Derailment, lateral load, bogie acceleration and body acceleration of numerical analysis results due to alignment were investigated. Finally, the influence of the alignment on safety and ride comfort for the KTX was evaluated. This study indicates that alignment irregularities have significant impacts on running safety, and that the criteria used to manage alignment irregularities should be restrictive to ensure the running safety of the KTX.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.4
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• pp.28-46
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• 2021

85-90% of the causes of traffic accidents are human factors, and autonomous vehicles with little free running distance can be an alternative to prevent traffic accidents caused by human factors. However, securing safety of autonomous vehicles should be preceded in order to reduce traffic accident damage through the introduction of autonomous vehicles. Therefore, it is necessary to verify whether the vehicle can respond appropriately to changes in the road and traffic environment through repeated and reproduced test runs in an environment similar to the actual road. In this study, K-City's development strategies for upgrading, differentiating, and systematic development were established by comparing and analyzing the current status of domestic and foreign testbeds and business environment analysis. Furthermore, we derive challenge tasks to achieve each strategy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.200-215
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• 2023

With the rapid advance of autonomous driving technology, the related vehicle market is experiencing explosive growth, and it is anticipated that the era of fully autonomous vehicles will arrive in the near future. However, along with the development of autonomous driving technology, questions regarding its safety and reliability continue to be raised. Concerns among technology adopters are increasing due to media reports of accidents involving autonomous vehicles. To promote the improvement of the safety of autonomous vehicles, it is essential to analyze previous accident cases and identify their causes. Therefore, in this study, we aimed to analyze the factors influencing the severity of autonomous vehicle accidents using previous accident cases and related data. The data used for this research primarily comprised autonomous vehicle accident reports collected and distributed by the California Department of Motor Vehicles (CA DMV). Spatial information on accident locations and additional traffic data were also collected and utilized. Given that the primary data used in this study were accident reports, a Poisson regression analysis was conducted to model the expected number of accidents. The research results indicated that the severity of autonomous vehicle accidents increases in areas with low lighting, the presence of bicycle or bus-exclusive lanes, and a history of pedestrian and bicycle accidents. These findings are expected to serve as foundational data for the development of algorithms to enhance the safety of autonomous vehicles and promote the installation of related transportation infrastructure.