• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.145-160
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• 2023

The purpose of this study was to compare and analyze diurnal thermal environments using Unmanned Aerial Vehicles(UAV)-derived physical parameters(NDVI, SVF) and ENVI-met modeling. The research findings revealed significant correlations, with a significance level of 1%, between UAV-derived NDVI, SVF, and thermal environment elements such as S↑, S↓, L↓, L↑, Land Surface Temperature(LST), and Tmrt. In particular, NDVI showed a strong negative correlation with S↑, reaching a minimum of -0.52^** at 12:00, and exhibited a positive correlation of 0.53^** or higher with L↓ at all times. A significant negative correlation of -0.61^** with LST was observed at 13:00, suggesting the high relevance of NDVI to long-wavelength radiation. Regarding SVF, the results showed a strong relationship with long-wave radiative flux, depending on the SVF range. These research findings offer an integrated approach to evaluating thermal comfort and microclimates in urban areas. Furthermore, they can be applied to understand the impact of urban design and landscape characteristics on pedestrian thermal comfort.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.39-48
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• 2024

A car horn serves a crucial safety role as a means of communication between drivers and a part that alerts pedestrians in advance. While previous studies have utilized finite element method and electric circuit model to simulate and analyze characteristics of the car horns, there remains a lack of research on design methods of a trumpet horn. This paper presents a design approach that predicts the operating frequency based on the acoustic reactance at the throat of the horn, once the vibrating part is determined. We deal with a horn combining both an exponential horn and a waveguide in the acoustic section, and confirm that the acoustic reactance at the horn throat measured by impedance tube experiment agrees well compared with the numerical result obtained using the finite element method. The resonance frequency of the car horn is predicted using the COMSOL Multiphysics finite element numerical analysis model, and the proposed design method is validated by measuring the operating frequency of the designed horn in a sound pressure experiment. As a result, the resonance measured in a semi-anechoic chamber environment by applying a DC voltage of 12 [V] excluding the holder occurs accurately within a few [Hz] of the design operating frequency. This paper discuss the design method of a trumpet horn from the perspective of the horn's acoustic reactance, and is expected to be useful for designing horn systems.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.135-135
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• 2018

한강의 잠수교는 평상시에는 사람과 차의 통행이 가능하나 예측수위가 5.5m일 경우, 보행자통제, 6.2m일 경우, 차량통제를 실시한다. 잠수교는 국토교통부의 홍수예보 지점은 아니지만 그 특수성으로 인해 정확한 홍수위 예측을 통해 선행시간을 확보할 필요가 있다. 일반적으로 하천 홍수위 예측을 위해서는 강우-유출 모형과 하도추적을 위한 수리모형을 결합한 모델링이 요구되나 잠수교는 하류부 조위로 인한 배수 및 상류부 팔당댐 방류량의 영향을 받아 물리적 수리 수문모형의 구축이 상당히 제약적이다. 이에 본 연구에서는 딥러닝 오픈 라이브러리인 Tensorflow 기반의 LSTM 심층신경망(Deep Neural Network) 모형을 구축하여 잠수교의 수위예측을 수행한다. LSTM 모형의 학습과 검증을 위해 2011년부터 2017년까지의 10분단위의 잠수교 수위자료, 팔당댐의 방류량과 월곶관측소의 조위자료를 수집한 후, 2011년부터 2016년까지의 자료는 신경망 학습, 2017년 자료를 이용하여 학습된 모형을 검증하였다. 민감도 분석을 통해 LSTM 모형의 최적 매개변수를 추정하고, 이를 기반으로 선행시간(lead time) 1시간, 3시간, 6시간, 9시간, 12시간, 24시간에 대한 잠수교 수위를 예측하였다. LSTM을 이용한 1~6시간 선행시간에 대한 수위예측의 경우, 모형평가 지수 NSE(Nash-Sutcliffe Efficiency)가 1시간(0.99), 3시간(0.97), 6시간(0.93)과 같이 정확도가 매우 우수한 것으로 분석되었으며, 9시간, 12시간, 24시간의 경우, 각각 0.85, 0.82, 0.74로 선행시간이 길어질수록 심층신경망의 예측능력이 저하되는 것으로 나타났다. 하천수위 또는 유량과 같은 수문시계열 분석이 목적일 경우, 종속변수에 영향을 미칠 수 있는 가용한 모든 독립변수를 데이터화하여 선행 정보를 장기적으로 기억하고, 이를 예측에 반영하는 LSTM 심층신경망 모형은 수리 수문모형 구축이 제약적인 경우, 홍수예보를 위한 활용이 가능할 것으로 판단된다.