• Proceedings of the KOR-KST Conference
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• 1999.10a
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• pp.141-146
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• 1999

본 연구는 평면곡선부의 차량속도변화와 교통사고에 대한 영향을 분석하기 위해 수행되었으며, 이를 위해 곡선반경별로 진입부 직선도로 - 곡선부 - 이탈부 직선도로로 구분하여 각 주행속도를 산정하였다. 본 연구에서는 선행차량에 의해 영향을 받지 않는 속도를 이용하여 평면곡선부 기하구조의 영향을 분석하였다. 이러한 곡선부의 주행 속도를 분석한 결과, 곡선반경별 최저속도와 그 속도가 나타나는 위치를 분석하였고, 해당 도로구간의 교통사고자료를 분석하여 평면곡선부의 차량주행속도변화와 교통사고 발생간의 관계를 규명하였다. 연구 결과는 다음과 같다. 첫째, 운전자들은 곡선시작지점에 도착하기 전에 충분히 속도를 줄이고, 곡선부에서 가장 낮은 속도가 나타나는 위치는 곡선시점의 후방에서 나타나고 이는 운전자의 시거에 의한 영향이다. 둘째, 곡선부에서의 속도변화폭이 클수록 사고위험성은 크고 실제 교통사고의 발생건수는 높다. 본 연구의 결과에 의해 도로 주행의 안전성을 고려한 평가지표를 개발할 수 있으며, 이에 의한 일관성 있는 도로설계를 유도할 수 있다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.91-104
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• 2022

The operational design domain (ODD) of autonomous vehicles needs to be expanded on highways and urban roads in light of the substantial commercialization of Level 3 autonomous vehicles. Therefore, this study developed a specific infrastructure autonomous vehicle-based cooperative driving service to ensure the driving safety of autonomous vehicles on city roads. The traffic operation efficiency, safety evaluation, and core evaluation indices for each service were selected and analyzed to study the effect of each service. The result of the analysis confirmed that the traffic operation efficiency and safety of autonomous vehicles were improved through the V2X communication-based autonomous cooperative driving service. On the whole, the significance of this study is in deriving the effect of the autonomous cooperative driving service based on V2X communication on urban roads with interrupting traffic flow.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.183-185
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• 1999

주행 경로 안내는 최근 국내에서도 활발히 연구되고 있는 지능형 교통 시스템(ITS)의 주요 기능 중 하나이다. 주행 경로 안내를 위해서는 대규모 도로망에서 신속하게 경로를 찾는 방법과 찾은 경로를 음성 또는 기호로 운전자에게 효율적으로 안내하는 방법등이 필요하다. 본 논문에서는 도로망의 계층성을 휴리스틱 정보로 활용하여 최단시간 경로를 효율적으로 찾는 방법을 제안한다. 제안된 방법은 고속국도, 국도 등의 고속 주행용 도로만으로 소규모 상위 계층 도로망을 만들고 이를 기존 도로망에 덧붙이는 방식으로 통합한다. 이 통합망에 상위 계층 도로망을 우선적으로 찾도록 구성한 A* 알고리즘을 수행하여 최단시간 경로를 찾는다. 또 경로 탐색용 그래프가 디스크에 저장된 경우에, 디스크 접근을 최적화하기 위한 데이터베이스 설계 및 디스크 접근 방법을 기술한다. 제안된 방법의 효율성을 검증하기 위해 서울시 도로망 데이터를 이용하여 실험한 결과, 제안된 방법을 통해 경로 탐색 소요 시간, 디스크 입출력 회수, 메모리 사용량 등을 75%이상 줄일 수 있었다.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.139-143
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• 2002

운전자가 고속도로와 시내도로에서 3시간 동안 주행했을 때 주관적인 운전 피로감 및 졸림감, 신체적인 불편도를 주관적 평가지를 이용하여 측정하였다. 실험결과 운전 피로감은 모든 주행조건에서 운전시간이 경과됨에 따라 유의하게 증가하였고 졸림감은 고속도로 주행시 운전시간에 따라 유의하게 증가하였으며 시내 주행에서 고속도로 주행보다 더 많은 신체부위에서 불편도가 높았다. 따라서 운전 피로는 운전시간에 따라 증가하고 고속도로에서는 졸음, 시내주행에서는 신체적인 불편으로 인한 피로가 증가함을 알 수 있었다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.4
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• pp.112-123
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• 2018

The C-ARS(Cooperative Automated Roadway System) refers to a road infrastructure system that links automated vehicles with road infrastructure and communicates with each other via V2X communication to support automated vehicles. The purpose of this study is to suggest a performance evaluation method of C-ARS service. This study exemplifies the 'Work zone information service' among I2V service that provide information to automated vehicles in road infrastructure. First, we define the requirements and service scope needed to check the use case analysis and service performance of the service, and propose an evaluation system for performance evaluation of these services. In addition, the evaluation system was used to verify the feasibility of evaluation through the field test of 'Work zone information service'.

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.77-91
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• 2023

Recently, there has been a worldwide increase in research and development on automated vehicles for commercialization. It is expected that the use of level 3 autonomous vehicles on continuous-flow roads will be introduced and will increase. Consequently, various studies have been conducted to investigate the impact of mixed traffic flow with automated vehicles based on the market penetration rate (MPR). However, these studies have been conducted independently, and the results have shown different trends. Therefore, this study attempted a quantitative analysis of the impact of automated vehicles on mixed traffic flow on uninterrupted roads through a meta-analysis. The results showed that the effect size estimated from an MPR of 75% or higher was statistically significant.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.4
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• pp.519-526
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• 2022

Basically, autonomous vehicles drive using road and traffic information collected by various sensors. However, it is known that there is a limitation to realizing fully autonomous driving with only such technologies and information. In recent, various efforts are being made to overcome the limitations of sensor-based autonomous driving, and efforts are also underway to utilize more specific and accurate road and traffic information, called local dynamic map (LDM). However, LDM-related data standards and specifications have not yet been sufficiently verified, and research on the spatiotemporal scope of LDM during autonomous driving is extremely limited. Based on this background, the purpose of this study is to identify these limitations through an analysis of previous LDM-related studies and to present a framework for calculating the spatiotemporal activation section of LDM-based road and traffic information.

• Journal of Korean Society of Transportation
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• v.33 no.5
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• pp.449-460
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• 2015

The reliability of air quality evaluation results for green transportation could be improved by applying correct emission factors. Unlike previous studies, which estimated emission factors that focused on vehicles in laboratory experiments, this study investigates emission factors according to road types and time using real driving data. The real driving data was collected using a Portable Activity Monitoring System (PAMS) according to road types and time, which it compared and analyzed fuel consumption from collected data. The result of the study shows that fuel consumption on national highway is 17.33% higher than the fuel consumption on expressway. In addition, the average fuel consumption of peak time is 4.7% higher than that of non-peak time for 22.5km/h. The difference in fuel consumption for road types and time is verified using ANOCOVA and MANOVA. As a result, the hypothesis of this study - that fuel consumption differs according to road types and time, even if the travel speed is the same - has proved valid. It also suggests correction factor of emission factors by using the difference in fuel consumption. It is highly expected that this study can improve the reliability of emissions from mobile pollution sources.

• Journal of Climate Change Research
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• v.4 no.4
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• pp.359-369
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• 2013

In this paper, correlations between driving parameters and $CO_2$ of light-duty vehicles have been analyzed. Three test vehicles equipped with PEMS (Portable Emission Measurement System) have been driven in real-road in urban areas of Seoul. Averaged vehicle speed, RPA(Relative Positive Acceleration) and stop ratio have been selected as main driving parameters. The analysis have been conducted in interrupted and uninterrupted road types. Averaged values in various driving conditions have been calculated with distance based moving averaging window method. The multiple linear regression method have been applied to account for correlation between driving parameters and $CO_2$ emissions. This approach has shown statistically that $CO_2$ emission per distance (g/km) have tendencies to be increased as decreased averaged vehicle speed and increased RPA and stop ratio. Compared with uninterrupted traffic, interrupted traffic have shown the lower vehicle speed and the higher RPA and stop ratio. These characteristics of driving parameters in interrupted traffic should cause the higher $CO_2$ emission per distance.