• Proceedings of the KAIS Fall Conference
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• 2009.05a
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• pp.821-824
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• 2009

고속도로를 주행 중인 차량에 강풍이 작용할 경우 차량의 주행안정성 저하와 운전자의 상황에 따른 치명적인 교통사고로 이어질 수 있다. 특히, 최근에 건설되거나 추진 중인 고속도로는 교량과 터널의 연속적인 조합으로 강풍발생위험에 노출되어 있어 적극적인 강풍저감 대책이 필요하다. 효과적인 방풍 시설 설치나 사전예고 시스템의 도입을 위해서는 차량의 동역학적 거동 분석, 주행 중인 차량의 위험도 판단기준 연구, 강풍위험지역의 정확한 풍속 추정기법 연구, 지능화된 방풍벽 개발, 방풍시설 설치판단 기준 제시, 합리적인 차량 속도 규제정책 등의 연구 개발이 수행되어야 한다. 본 연구에서는 강풍 발생 구간을 주행하는 차량의 동역학적 거동모델을 제시하고, 이를 기반으로 기존에 제안된 위험도 판단기준의 합리성을 제고하였으며, 운전자의 상황을 고려한 현실적인 차량주행위험도 판단기준 제시를 목표로 하였다.

• Journal of Korean Society of Transportation
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• v.29 no.1
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• pp.95-102
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• 2011

This paper presents a Driving Risk Model (DRM) based on driving speed choices using an Ordered Response Probit (ORP) model. The DRM is conceptualized based on the relation between speed deviation and the occurrence of crashes found by Solomon. The impacts of various driving risk factors are revealed by applying the DRM to evaluate the effectiveness of In-Vehicle Traffic Warning Information (IVTWI) in expressway driving. Regarding driving risk, the results show that: (1) the risk is lower among male drivers, those with more driving experience and those with less accident history, (2) the risk is higher when driving takes place on wet road surface, in the afternoon, and under conditions of low traffic volume, and (3) the risk is also higher on both downgraded and long curve sections. Additionally, the results provide evidence that provision of IVTWI can decrease the driving risk. The proposed DRM provides a solution for assessing the traffic safety impacts of countermeasures on roadways when there is a shortage of traffic accidents data.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.62-73
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• 2022

As the competition among the autonomous vehicle (AV, here after) developers are getting fierce, Korean government has been supporting developers by deregulating safety standards and providing financial subsidies. Recently, some OEMs announced their plans to market Lv3 and Lv4 automated driving systems. However, these market changes raised concern among public road management sectors for monitoring road conditions and alleviating hazardous conditions for AVs and human drivers. In this regards, the authors proposed a methodology for monitoring road infrastructure to identify hazardous factors for AVs and categorizing the hazards based on their level of impact. To evaluate the degrees of the harm on AVs, the authors suggested a methodology for managing road hazard factors based on vehicle performance features including vehicle body, sensors, and algorithms. Furthermore, they proposed a method providing AVs and road management authorities with potential risk information on road by delivering them on the monitoring map with node and link structure.

• Smart Media Journal
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• v.13 no.10
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• pp.9-18
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• 2024

E-scooters are eco-friendly and convenient, making their rental services highly popular. However, simultaneously, due to issues such as a surge in user numbers and a lack of user awareness about relevant traffic laws, related accident rates have increased tenfold in the last five years. As a result, dangerous riding of e-scooters is being presented as a new social issue. This study proposes a framework for detecting dangerously operating e-scooters in a fixed single-camera environment, which is cost-effective and conducive to accident detection. The proposed method uses object detection and tracking technology to identify people and e-scooters, simultaneously detecting multiple riders, helmet non-use, and sidewalk riding. For validation, it achieved excellent dangerous behavior detection performance in 17 diverse scenarios directly generated. Furthermore, compared to existing methods, it could detect more dangerous riding behaviors and provided detailed information, such as separately mapping dangerous riding results for each subject during multiple-rider situations. These results are expected to play a crucial role in enhancing urban traffic safety.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.192-207
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• 2023

Commercialization of level-4 (Lv.4) autonomous driving applications requires the definition of a safe road environment under which autonomous vehicles can operate safely. Thus, a risk assessment model is required to determine whether the operation of autonomous vehicles can provide safety to is sufficiently prepared for future real-life traffic problems. Although the risk factors of autonomous vehicles were selected and graded, the decision-making method was applied as qualitative data using a survey of experts in the field of autonomous driving due to the cause of the accident and difficulty in obtaining autonomous driving data. The fuzzy linguistic representation of decision-makers and the fuzzy analytic hierarchy process (AHP), which converts uncertainty into quantitative figures, were implemented to compensate for the AHP shortcomings of the multi-standard decision-making technique. Through the process of deriving the weights of the upper and lower attributes, the road alignment, which is a physical infrastructure, was analyzed as the most important risk factor in the operation risk of autonomous vehicles. In addition, the operation risk of autonomous vehicles was derived through the example of the risk of operating autonomous vehicles for the 5 areas to be evaluated.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2023.11a
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• pp.271-272
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• 2023

본 논문에서는 대학 캠퍼스 내 배달로봇 사용 시 환경적 특성에 의한 유해위험요인을 사전에 발굴하고 로봇의 출발지에서 목적지까지 발생할 수 있는 위험을 단계별로 추정·판단하여 위험을 감소에 대한 필요성에 관한 연구를 수행하였다. 캠퍼스 내 주행 구간별 유해위험요인을 파악 및 단계별 위험성 추정을 위하여 배달로봇을 사용하기 전 위험성을 사전에 확인할 수 있는 위험성평가의 필요성을 제시한다. 배달로봇 시장의 확대로 인한 사람과의 접촉 증가에 비하여 안전대책 연구가 미흡한 실정이며, 로봇의 기술적 진보와 사용자들의 정서적 수준에 뒤처지지 않는 적절한 안전대책을 마련해야 한다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.42-60
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• 2024

This study analyzed the characteristics of mixed traffic flows with autonomous vehicles on highway weaving sections and assessed the safety of vehicle-following pairs based on surrogate safety indicators. The intelligent driver model (IDM) was utilized to emulate the driving behavior of autonomous vehicles, and the weaving sections were divided into lengths of 300 and 600 meters for analysis within a micro-traffic simulation (VISSIM). Although significant differences were found in the average speed, density, and headway between the two sections through t-test results, no significant differences were observed when comparing the number of conflicts per indicator and the vehicle-following pair. Four safety indicators were selected for the mixed traffic evaluation based on their ability to represent risk levels similar to those perceived by drivers. The safety analysis, based on the selected four indicators, determined that autonomous vehicles following other autonomous vehicles were the safest pairing. Future research should focus on integrating these indicators into a single comprehensive index for analysis.

• Journal of Korean Society of Transportation
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• v.25 no.5
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• pp.149-160
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• 2007

Automobile black boxes store and provide accident and driving information. The accident and driving information can be utilized to build scientific traffic-event database and can be applied in various industries. The objective of this study is to develop a recognition system of dangerous driving through analyzing the driving characteristic patterns. In this paper, possible dangerous driving models are classified into four models on the basis of vehicle behaviors(acceleration, deceleration, rotation) and accident types from existing statistical data. Dangerous driving data have been acquired through vehicle tests using automobile black boxes. Characteristics of driving patterns have been analyzed in order to classify dangerous driving models. For the recognition of dangerous driving, this study selected critical value of each dangerous driving model and developed the recognition algorithm of dangerous driving. The study has been verified by the application of recognition algorithm of dangerous driving and vehicle tests using automobile black boxes. The presented recognition methods of dangerous driving can be used for on-line/off-line management of drivers and vehicles.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.26-41
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• 2024

To ensure safer road conditions, understanding the human factors influencing driving behavior is crucial. However, there are many difficulties in deriving the characteristics of individual human factors that affect actual driving behaviors. Therefore, this study analyzes self-reported dangerous-driving propensities in order to explore potential correlations with drivers' behaviors. The goal is to propose a method for assessing driving tendencies based on varying traffic scenarios. The study employed a questionnaire to gauge participants' propensity to drive dangerously, utilizing a simulator to analyze their driving behaviors. The aim is to determine any notable connections between dangerous-driving propensity and specific driving behaviors. Results indicate that individuals exhibiting a high propensity for reckless driving, as identified by the Korean DBQ, tend to drive at higher speeds and display more aggressive acceleration patterns. These findings contribute to a potential method for assessing reckless driving drivers.

• International Journal of Highway Engineering
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• v.13 no.3
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• pp.139-146
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• 2011

Travelling vehicles on roads may slip or overturn due to strong cross wind. This paper presents the path deviation equation and the overturning equation of vehicle, and the process of evaluating the cross wind risk. Case studies for cars and trucks are carried out. It explains the mechanism why the deviation occurs according to the types of vehicles. It shall help to prepare the measures for reducing the risk of travelling vehicles in high wind speeds.