• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.944-956
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• 2013

Drivers need lots of information when they drive on the highway, however it is necessary and important to be provided information which is appropriate formation. In order to offer the suitable information, adequate size, quantity, and frequency of provided information are required for the drivers. To evaluate propriety of the expressed message of VMS that provides real-time traffic conditions, first of all, the amount of message about legibility distance and viewing should be estimated. In this research, drivers' characteristics of VMS message design were also reviewed to enhance the efficiency of VMS. And legibility distance, the amount of viewing information, and ratio of viewing information were analyzed on the currently operating VMS. The results of this study proposed that the appropriate size, quantity, and frequency were concluded by the legibility and memory of message on the real driving conditions. Consequently, these design methods of VMS could be expected to improve the transmitting capability of highway information to drivers.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.3
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• pp.87-93
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• 2015

The accidents caused by drivers while driving are considered as the major causes along with other causes such as conditions of roads, weather and cars. In this study, we investigated the driver's workloads under three different driving conditions (Weather, Driving time zone, and Traffic density) through analyzing biological signals obtained from a car driving simulator system. The proposed method is able to detect R waves and R-R interval calculation in the ECG. Heart rate variability (HRV) was investigated for the time domain to determine the changes in driver's conditions.

• Journal of Korean Society of Transportation
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• v.18 no.3
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• pp.87-100
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• 2000

각 지역특성에 맞는 ATIS사업이 실현되기 위해서는 각 지역 통행자들의 노선선택 및 전환행태를 정확히 파악하는 것이 필요하다. 이에 본 연구에서는 경상대를 연구대상으로 하여 대학출근운전자들의 노선선택 및 전환 행태를 정확히 파악하고 이들을 모형화하였다. 본 연구 대상지의 경우, 2개의 주 출근노선이 있으며 하나는 시내통행노선(노선 1)이고 다른 하나는 시외곽 통행노선(노선 2)이다. 노선1은 노선2에 비해 연장은 짧은 반면에 통행시간은 길며 신호교차로수. 우회전수도 많다. 먼저, 운전자의 노선선택행태모형을 통해 해석된 결과를 보면 시내노선에 대한 외곽노선의 상대적 효용이 아주 높으며, 전체적으로 출근운전자들은 짧은 통행시간을 선호하는 것으로 나타났다. 또한, 출근소요시간이 길고 라디오정보의 이용빈도가 높을수록 시내노선을 이용할 확률이 크며, 반면에 남성과 교직원인 운전자는 외곽노선을 이용할 확률이 큰 것으로 나타났다. 다음으로 행태조사에 기초한 노선전환행태모형을 통해 해석된 결과를 보면 연령, 출근시간, 라디오정보의 이용빈도들이 전환성향에 유의한 영향을 가져오는 것으로 분석되었다. 가상의 교통정보제공시의 운전자의 노선전환을 모형화한 노선전환의사모형에서는 대개의 정보에 대해 운전자가 노선전환을 하는 것으로 나타났다. 이 모형에서 지체길이에 따른 전환경향을 보면 지체의 길이가 길수록 전환경향이 높아 30분정도의 지체길이에서는 반드시 변경하는 것으로 나타났다. 본 연구대상 운전자의 경우 전반적으로 기술적인 유고(Incident)정보보다는 정량적인 지체정보에 더 민감한 것으로 나타났다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.603-604
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• 2011

자전거 운전은 본질적인 불안정한 시스템으로 자전거의 안전한 운전을 위해서는 지속적인 운전자의 상호작용으로 인하여 자전거의 안전성을 확보할 수 있다. 그것은 자전거의 운전이 운전자의 운동과 매우 밀접한 연관이 있다는 것을 알 수 있으며, 현재 자전거의 특성뿐 아니라 운전자의 운전에 대한 연구가 진행되고 있지만 정확한 자전거의 주행 안정성에 대하여 연구는 계속적으로 진행되고 있다. 본 논문에서는 자전거 운전자의 몸무게와 신장과 같은 사람의 인체 정보를 이용하여 적절한 안장 높이와 자전거 바퀴 사이의 거리 및 핸들의 높이를 조절할 수 있는 맞춤형 자전거 관리 시스템을 제시하고자 한다. 또한, 운전자 맞춤형 자전거의 설계는 실제 자전거 운전데이터와 영상데이터를 이용하여 적합한 자전거 모델을 제시하고 추출된 데이터를 통하여 분석된 결과를 제시하고자 한다.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.9
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• pp.8-16
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• 2008

We studied drivers sight and physical status change by setup type of road lighting system. Influences on drivers emotional and motional change of lighting condition was investigated and function of road lighting setup position at night was analysed. Specially we analyzed influences of line type low hight lighting have influence on driver sight.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1D
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• pp.11-19
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• 2008

Currently, existing car following models among several basic systems of advanced vehicle systems are almost developed related to the physical relation between two vehicles except for the driver's behavior or environmental factors. But the consideration of driver's character and environmental factors on driving are very essential factors for actual application. Hence, we suggested calibrating the degree of driver's discomfort on driving that is the former study to develop a new car following model of advanced vehicle to use in actuality. The degree of driver's discomfortness (Measure-of-Alarmness; MOA)is measured related to the relationship between the following vehicle and the preceding vehicle, the environmental factors and driver's characters in ubiquitous traffic. We made up questions to drivers to obtain the general and the objective measurement of driver's MOA. And the fuzzy logic model for measurement of MOA was constructed based on the results of survey. We verified the suitability of fuzzy logic model through the computation of MOA with several scenarios. And we measured the quantitative degree of driver's discomfortness on car following related to several factors which affect drivers. In accordance with this study, development of car following model applying driver's MOA will promote the actual application of advanced vehicle more effectively than the existing models. Finally, we thought the measurement of driver's MOA will be applied significantly to evaluate safety and comfort of drivers on driving.

• Journal of the Korea Convergence Society
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• v.8 no.9
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• pp.285-292
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• 2017

This paper describes a recursive least-squares based convergent algorithm for driving characteristic classification for personalized autonomous driving. Recently, various researches on autonomous driving technology have been conducted for level 4 fully autonomous driving. In order for commercialization of the autonomous vehicle, personalized autonomous driving is required to minimize passenger's insecureness to the autonomous vehicle. To address this problem. this study proposes mathematical model that represents driving characteristics and recursive least-squares based algorithm that can estimate the defined characteristics. The actual data of two drivers has been used to derive driving characteristics and the hypothesis testing method has been used to classify two drivers. It is shown that the proposed algorithms can derive driving characteristics and classify two drivers reasonably.

• Journal of Korean Society of Transportation
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• v.21 no.3
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• pp.97-106
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• 2003

When a route choice is done under uncertainty, a driver has some expectation of traffic conditions that will occur according to the route chosen. This study tries to build a framework in which we can observe the learning behavior of the drivers' expectations of the travel time under nonstationary environment. In order to investigate how drivers have their subjective expectations on traffic conditions in response to public information, a numerical experiment is carried out. We found that rational expectations(RE) formation about the route travel time can be expressed by the adaptive expectation model when the travel time changes in accordance with the nonstationary process which consists of permanent shock and transient shock. Also, we found that the adaptive parameter of the model converges to the fixed value corresponding to the route conditions.

• Database Research
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• v.34 no.3
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• pp.45-57
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• 2018

The drowsiness that occurs in the driving is a very dangerous driver condition that can be directly linked to a major accident. In order to prevent drowsiness, there are traditional drowsiness detection methods to grasp the driver's condition, but there is a limit to the generalized driver's condition recognition that reflects the individual characteristics of drivers. In recent years, deep learning based state recognition studies have been proposed to recognize drivers' condition. Deep learning has the advantage of extracting features from a non-human machine and deriving a more generalized recognition model. In this study, we propose a more accurate state recognition model than the existing deep learning method by learning image and PPG at the same time to grasp driver's condition. This paper confirms the effect of driver's image and PPG data on drowsiness detection and experiment to see if it improves the performance of learning model when used together. We confirmed the accuracy improvement of around 3% when using image and PPG together than using image alone. In addition, the multimodal deep learning based model that classifies the driver's condition into three categories showed a classification accuracy of 96%.

• The Journal of the Korea Contents Association
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• v.14 no.7
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• pp.151-160
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• 2014

The purpose of this study is to deduce improvement plan through analysis of the type and characteristics of traffic accidents caused by elderly drivers, and to establish the traffic safety policies for the elderly drivers. The analyzed result of road traffic accident situation are as follows. Firstly, Traffic accidents caused by elderly drivers were frequent on local roads of poor traffic safety facilities such as lighting. Secondly, The most frequent time zone of Traffic accidents caused by elderly drivers was 18:00 to 20:00, this time zone was mainly darkened. Thirdly, Traffic accidents are often caused by lack of attention occurred by no implementation of safe driving. Institutional Improvement of cosideration for elderly drivers and Traffic Safety Facilities maintenance considering regional particularities is needed for reducing traffic accidents and the safety of our society. Their driving skills of elderly drivers should test themselves constantly, and elderly drivers must hold down driving yourself using the alternative transportation.