• Journal of Korean Institute of Industrial Engineers
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• v.37 no.1
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• pp.30-40
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• 2011

In recent years, driver information systems (DIS's) became popular and the use of DIS's increased significantly. A majority of DIS's provides touch-screen interfaces because of intuitiveness of the interaction and the flexibility of interface design. In many cases, touch-screen interfaces are mainly manipulated by fingers. In this case, investigating the effect of touch-key sizes on usability is known to be one of the most important research issues, and lots of studies address the effect of touch-key size for mobile devices or kiosks. However, there is few study on DIS's. The importance of touch-key size study for DIS's should be emphasized because it is closely related to safety issues besides usability issues. In this study, we investigated the effect of touch-key sizes of DIS's while simulated driving (0, 50, and 100km/h) considering driving safety (lateral deviation, velocity deviation, total glance time, mean glance time, total time between glances, mean number of glances) and usability of DIS's (task completion time, error rate, subjective preference, NASA TLX) simultaneously. As a result, both of driving safety and usability of DIS's increased as driving speed decreased and touch-key size increased. However, there were no significant differences when touch-key size is larger than a certain level (in this study : 17.5mm).

• Journal of the Korean Society of Safety
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• v.27 no.2
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• pp.117-126
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• 2012

Understanding driver's characteristic of visual activity is important process because driver depends on a visual signal more than 90% for getting outside information needed to drive, thus a series of driving, including perception, judgement, and activity, is completed. This study analyzes quantified driver's sight range in curved section where recognition of various information is critical due to biggest speed change among sections. Simulation is utilized for this study because of safety problem on field experiment and difficulties in using equipment. Building 6 roads that have different in curve radius by virtual driving map, experiment is carried out recruiting 30 people. Through analytical researches, it shows that drivers keep an eye on direction of driving, and driver's visual range is narrowed on left curve than right curve, and the more curve radius become small, the more drivers see in narrow angle.

• ETRI Journal
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• v.38 no.4
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• pp.757-766
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• 2016

This study was conducted to examine the usage effects of an augmented reality head-up display (AR-HUD) system on the risk perception and psychological changes of drivers. To do so, we conducted an experiment to collect the driver response times for vehicles and pedestrians as their risk perception behavior, and used a driving behavior determinants questionnaire consisting of Problem Evading, Benefits/Sensation Seeking, Anti-Personal Anxiety, Anti-Personal Angry, and Aggression factors for collecting the psychological characteristics of the drivers. Thirty drivers were randomly assigned into an in-vehicle AR-HUD using group and a control group. As a result, the Anti-Personal Anxiety and Anti-Personal Angry factors were negatively correlated with the response time for the control group. In contrast, these results were not shown for the in-vehicle AR-HUD system using group. These results indicate that the in-vehicle AR-HUD system may partially induce a relaxation of tension or stress for drivers with a high level of interpersonal anxiety. Therefore, the in-vehicle AR-HUD system might contribute to not only the visual safety driving behaviors of drivers, but also to their psychological driving safety with specific characteristics.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.2
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• pp.164-170
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• 2013

The latest vehicle yields a superior safety and reduction of driving burden by monitoring the driving state of vehicle and its environment with various sensors. To detect other vehicles and objects of the rear left and right-side blind spot area of driver, provide the information about a existence of objects inside the blind spot, and give a signal to avoid collision, this study proposes the intelligent outside rear-view mirror system. This study proposes SILS system with PreScan and Matlab/Simulink to verify practical applicability of developed BSDS. PreScan yields realistic driving environments and road conditions and vehicle model dynamics and collision warning is controlled by Matlab/Simulink.

• Journal of Auto-vehicle Safety Association
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• v.16 no.1
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• pp.35-41
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• 2024

In order to verify autonomous driving scenarios and safety, a lot of driving and accident data is needed, so various organizations are conducting classification and analysis of traffic accident types. In this study, it was determined that accident recording devices such as EDR (Event Data Recorder) and DSSAD (Data Storage System for Automated Driving) would become an objective standard for analyzing the causes of autonomous vehicle accidents, and traffic accidents that occurred from 2015 to 2020 were analyzed. Using the database system of IGLAD (Initiative for the Global Harmonization of Accident Data), approximately 360 accident data of EDR-equipped vehicles were classified and their characteristics were analyzed by comparing them with accident types of ADAS (Advanced Driver Assistance System)-equipped vehicles. It will be used to develop autonomous vehicle accident investigation guidelines in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.235-249
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• 2020

The Ministry of Land, Infrastructure and Transport established safety standards for Level 3 autonomous vehicles for the first time in the world in December 2019, and specified the safety standards for conditional autonomous driving systems. Accordingly, it is necessary to analyze the influence of various driving environments on take-over. In this study, using a driving simulator, we investigated how traffic conditions and weather conditions affect take-over time and stabilization time. The experimental procedure was conducted in the order of preliminary training, practice driving, and test driving, and the test driving was conducted by dividing into a traffic density and geometry experiment and a weather environment experiment. As a result of the experiment, it was analyzed that the traffic volume and weather environment did not affect the take-over time and take-over stabilization time, and only the curve radius affects take-over stabilization time.

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.182-199
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• 2023

Since 2021, government departments have been promoting Automated Driving Technology Development and Innovation Project as national research and development(R&D) project. The automated vehicles and service technologies developed as part of these projects are planned to be subsequently provided to the public at the selected Living Lab City. Therefore, it is important to determine a spatial area and operation section that enables safe and stable automated driving, depending on the purpose and characteristics of the target service. In this study, the static Operational Design Domain(ODD) elements for Level 4 automated driving services were reclassified by reviewing previously published papers and related literature surveys and investigating field data. Spatial analysis techniques were used to consider the reclassified ODD elements for level 4 in the real area of level 3 automated driving services because it is important to reflect the spatial factors affecting safety related to real automated driving technologies and services. Consequently, a total of six driving mode changes(disengagement) were derived through spatial information analysis techniques, and the factors affecting the safety of automated driving were crosswalk, traffic light, intersection, bicycle road, pocket lane, caution sign, and median strip. This spatial factor analysis method is expected to be useful for determining special areas for the automated driving service.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.51-63
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• 2022

Convoy driving is one of the technologies of multi-vehicle cooperation driving along with platoon driving. All over the world, research on vehicle control mechanisms to maintain vehicle formation during convoy driving convoy driving has been actively conducted and in Europe's Autonet 2030 project has developed and demonstrated convoy driving services for highways. But, even the concept of convoy driving is still insufficient in Korea. Therefore, in this study, the concept of convoy driving service was established and scenarios and communication messages for service application on urban roads were developed. And its effectiveness was verified through simulation analysis. As a result of comparing and analyzing individual vehicle cooperative driving and convoy driving for the blind spot support service and dilemma zone safety support service, which are representative V2I cooperative driving services on urban roads, the number of conflicts(indicator of traffic safety) and delays and stops(indicator of traffic efficiency) are significantly improved in convoy driving compared to individual vehicle cooperative driving.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.119-128
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• 2022

Recently, due to the development of related technologies for autonomous vehicles, driving work is changing more safely. However, the development of support technologies for level 5 full autonomous driving is still insufficient. That is, even in the case of an autonomous vehicle, the driver needs to drive through forward attention while driving. In this paper, we propose a method to monitor driving tasks by recognizing driver behavior. The proposed method uses pre-trained deep convolutional neural network models to recognize whether the driver's face or body has unnecessary movement. The use of pre-trained Deep Convolitional Neural Network (DCNN) models enables high accuracy in relatively short time, and has the advantage of overcoming limitations in collecting a small number of driver behavior learning data. The proposed method can be applied to an intelligent vehicle safety driving support system, such as driver drowsy driving detection and abnormal driving detection.