• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.5
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• pp.23-29
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• 2015

In this study, human is the subject of driving a car, the actual EEG is a biological information in a number of reactions that are displayed while driving the vehicle by using a measuring device, occurs during travel of the road EEG to be collected, number of experiments the collected material on the basis of changes associated with running time, extracts the factors such as changes due to road geometry, and analysis was performed. The required changes in the EEG occurring during traveling experiment analysis alpha (${\alpha}$) waves, beta (${\beta}$) wave, after the primary extraction in the form of gamma (${\gamma}$) faction, the brain wave frequency of the entire period of the experiment change rate extracts, to calculate the change in frequency in response to EEG characteristics by applying the regression model to observe a learning effect in response to an increase in the number of experiments, as a result, depending on the number of experiments, EEG changes due to individual differences. The show, by repeatedly driving a section like this, it was possible to verify that comfortably travels driver accustomed in accordance with the stored road geometry and signal, safety facilities.

• Science of Emotion and Sensibility
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• v.23 no.2
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• pp.103-120
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• 2020

Using driving simulation, this study investigated the effects of driver's trust in the adaptive cruise control (ACC) system and road density on driver's workload and situation awareness. The drivers were allocated into one of four experimental conditions manipulated by ACC system trust level (trust-increased vs. trust-decreased) and road congestion (high vs. low). The workload and situational awareness of the participants were measured as dependent variables. The results showed followings. First, trust-decreased group for the ACC system had significantly lower trust scores for the system in all of the measurement items, including reducing the driving load and securing safe driving due to the use of this system, than the trust-increased group. Second, the trust-decreased group showed a slower reaction time in the secondary tasks and higher subjective workload than trust-increased group. Third, in contrast, the situational awareness for the driving situation was significantly higher in the trust-decreased group than trust-increased group. The results of this study showed that the driver's trust in the ACC system can affect the various information processing performed while driving. Also, these results suggest that trust in the user's system should be considered as an important variable in the design of an automated driving assistance system.

• Journal of the Korea institute for structural maintenance and inspection
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• v.21 no.6
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• pp.185-195
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• 2017

In recent years, unexpected bridge fire accidents have increased because of augmenting the number of traffic volumes and hazardous materials by the increment in traffics and distribution business. Furthermore, in accordance with the effort of using the under space of bridges, the ratio of occupied by combustible materials like oil tanker or lorry has been increased. As a result, the occurrence of bridge fire has been growing drastically. In order to mitigate the accident of bridge fire, risk assessment of bridge fire has been studied, however, practical risk models considering safety from users' viewpoints were scarce. This study represented quantitative risk assessment model applicable to national road bridges in Korea. The primary factors with significant impacts on bridge fire accidents was chosen such as clearance height, materials of bridges, arrival time of fire truck and fire intensity. The selected factors were used for Fire Dynamics Simulation (FDS) and the peak temperature calculated by FDS in accordance with the fire duration and fire intensity. The risk assessment model in bridge fire reflected the FDS analysis results, the fire damage criteria, and the grade of fire truck arrival time was established. Response plans for bridge fire accidents according to the risk assessment output has been discussed. Lastly, distances between bridges and fire stations were calculated by GIS network analysis. Based on the suggested assessment model and methodology, sample bridges were selected and graded for the risk assessment.

• Journal of Digital Convergence
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• v.15 no.12
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• pp.313-321
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• 2017

Traffic accidents in the country have steadily increased. Although IOT technologies have been applied so as to prevent the primary accident, practical solutions to prevent the secondary accident have not been suggested. A general guideline is simply recommended. In this paper, utilizing existing communication technology, we implement a proposed model and its simulation to prevent the secondary accident. When it is possible for a driver to secure visibility, the secondary accident can be prevented; In areas like tunnel, mountain terrain, and curve road with heavy traffic, where the driver has difficulty in securing the visibility, the secondary accident rates after the primary accident have been increasing. Therefore, we implement an accident prevention system that determines the primary accident utilizing sensor technology and prevents the secondary accident communicating through V2V or V2I. After the simulation, we found that the proposed model and the existing model made no difference with regard to the secondary accident rates when the visibility of the driver is secured; With the application of the proposed model, however, the accident rates decreased for 3-7 percent even though the visibility and communication were not secured.

• Journal of Korean Society of Transportation
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• v.34 no.4
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• pp.341-353
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• 2016

Highway is mainly in charge of middle-long distance of vehicular travel. Trip length has shown a growing trend due to increased commute distances by the relocation of public agencies. For this reason, the proportion of driver-driven accidents, caused by their fatigue or sleepiness, are very high on highways. However, existing studies related to accident prediction have mainly considered external factors, such as road conditions, environmental factors and vehicle factors, without driving behavior. In this study, we suggested an accident index (FDR, Fatigued Driving Rate) based on traffic behavior using large-scale Car Navigation path data, and exlpored the relationship between FDR and traffic accidents. As a result, FDR and traffic accidents showed a high correlation. This confirmed the need for a paradigm shift (from facilities to travel behavior) in traffic accident prediction studies. FDR proposed in this study will be utilized in a variety of fields. For example, in providing information to prevent traffic accidents (sleepiness, reckless driving, etc) in advance, utilization of core technologies in highway safety diagnostics, selection of priority location of rest areas and shelter, and selection of attraction methods (rumble strips, grooving) for attention for fatigued sections.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.477-484
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• 2019

As the residence time in the vehicle increases, the passenger desires a pleasant and stable riding environment in addition to the high driving performance of the vehicle. The windshield defrosting performance is one of the performance requirements that is essential for driver's safe driving. In order to improve the defrosting performance of the windshield of a vehicle, relevant elements such as the shape of the defrost nozzle should be appropriately designed. In this paper, CFD based numerical analysis is conducted to improve defrost performance of small electric vehicles. The defrost performance analysis was performed by changing the angle of the defrost nozzle and the guide vane that spray hot air to the windshield of the vehicle. Numerical simulation results show that the defrosting performance is best when the defrost nozzle angle is $70^{\circ}$ and the guide vane installation angle is $60^{\circ}$. Based on the analytical results, the defrosting experiment was performed by fabricating the defrost nozzle and the guide vane. As a result of the experiment, it is confirmed that the frost of windshield is removed by 80% within 20 minutes, and it is judged that the defrost performance satisfying the FVMSS 103 specification is secured.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.539-549
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• 2024

This study employed text mining techniques like frequency analysis, word clouds, and LDA topic modeling to assess consumer satisfaction and dissatisfaction with Hyundai Motor Company in the Chinese market, compared to brands such as Toyota, Volkswagen, Buick, and Geely. Focusing on compact vehicles from these brands between 2021 and 2023, this study analyzed customer reviews. The results indicated Hyundai Avante's positive factors, including a long wheelbase. However, it also highlighted dissatisfaction aspects like Manipulate, engine performance, trunk space, chassis and suspension, safety features, quantity and brand of audio speakers, music membership service, separation band, screen reflection, CarLife, and map services. Addressing these issues could significantly enhance Hyundai's competitiveness in the Chinese market. Previous studies mainly focused on literature research and surveys, which only revealed consumer perceptions limited to the variables set by the researchers. This study, through text mining and comparing various car brands, aims to gain a deeper understanding of market trends and consumer preferences, providing useful information for marketing strategies of Hyundai and other brands in the Chinese market.

• The Journal of Bigdata
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• v.2 no.2
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• pp.1-7
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• 2017

Intelligent transportation system (ITS) has been introduced to maximize the efficiency of operation and utilization of the urban traffic facilities and promote the safety and convenience of the users. With the expansion of ITS, various traffic big data such as road traffic situation, traffic volume, public transportation operation status, management situation, and public traffic use status have been increased exponentially. In this paper, we derive structural characteristics of urban traffic according to the vehicle flow by using big data network analysis. DSRC (Dedicated Short Range Communications) data is used to construct the traffic network. The results can help to understand the complex urban traffic characteristics more easily and provide basic research data for urban transportation plan such as road congestion resolution plan, road expansion plan, and bus line/interval plan in a city.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.10
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• pp.162-170
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• 2013

In recent years, the interests and needs of the Pedestrian Protection System (PPS), which is mounted on the vehicle for the purpose of traffic safety improvement is increasing. In this paper, we propose a pedestrian candidate window extraction and unit cell histogram based HOG descriptor calculation methods. At pedestrian detection candidate windows extraction stage, the bright ratio of pedestrian and its circumference region, vertical edge projection, edge factor, and PCA reconstruction image are used. Dalal's HOG requires pixel based histogram calculation by Gaussian weights and trilinear interpolation on overlapping blocks, But our method performs Gaussian down-weight and computes histogram on a per-cell basis, and then the histogram is combined with the adjacent cell, so our method can be calculated faster than Dalal's method. Our PCA reconstruction error based pedestrian detection candidate window extraction method efficiently classifies background based on the difference between pedestrian's head and shoulder area. The proposed method improves detection speed compared to the conventional HOG just using image without any prior information from camera calibration or depth map obtained from stereo cameras.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.4
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• pp.13-24
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• 2008

Real-time traffic data are readily obtainable by traffic surveillance systems of intelligent transportation systems (ITS). Such data greatly support further applications in the field of traffic operations, planning, and safety. However, traffic data should be appropriately processed to fully exploit the benefits of data collection capability. Rather than developing individual data processing techniques, which is major concern of existing studies, this study proposes a novel methodology for evaluating data processing techniques in an integrated manner. Also, a tool for implementing the proposed methodology is developed. Users can extract useful and more reliable traffic data based upon their ultimate purpose of data usage by the evaluation tool developed in this study. Actual freeway traffic data are, as an example, fed into the evaluation tool, and results are discussed.