• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.299-312
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• 2021

Autonomous vehicles using V2X can drive safely information on areas outside the sensor coverage of autonomous vehicles conventional autonomous vehicles. As V2X technology has emerged as a key component of autonomous vehicles, research on V2X security is actively underway research on risk analysis due to failure of V2X communication is insufficient. In this paper, the service scenario and function of autonomous driving system V2X were derived by presenting the intersection scenario of the autonomous vehicle, the malfunction was defined by analyzing the hazard of V2X. he ISO26262 Part3 process was used to analyze the risk of malfunction of autonomous vehicle V2X. In addition, a fault injection scenario was presented to verify the fail-safe of the simulation-based intersection scenario.

• Journal of Drive and Control
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• v.18 no.4
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• pp.9-18
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• 2021

This study proposes a method for creating learning datasets to recognize obstacles using deep learning algorithms in automated construction machinery or an autonomous vehicle. Recently, many researchers and engineers have developed various recognition algorithms based on deep learning following an increase in computing power. In particular, the image classification technology and image segmentation technology represent deep learning recognition algorithms. They are used to identify obstacles that interfere with the driving situation of an autonomous vehicle. Therefore, various organizations and companies have started distributing open datasets, but there is a remote possibility that they will perfectly match the user's desired environment. In this study, we created an interface of the virtual simulator such that users can easily create their desired training dataset. In addition, the customized dataset was further advanced by using the RDBMS system, and the recognition rate was improved.

• 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.

• Journal of Korean Society of Transportation
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• v.35 no.6
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• pp.545-556
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• 2017

Various assessment indexes have been developed and utilized to evaluate the driver workload. However, existing workload assessment indexes do not fully reflect driving habits and driving patterns of individual drivers. In addition, there exists significant differences in the amount of workload experienced by a driver and the ability to overcome the driver's workload. To overcome these limitations associated with existing indexes, this study has developed a novel workload assessment index to reflect an individual driver's driving pattern. An average of the absolute values of the steering velocity for each driver are set as a threshold value in order to reflect the driving patterns of individual drivers. Further, the sum of the areas of the steering velocities exceeding the threshold value, which is defined as erratic steering area (ESA) in this study, was quantified. The developed ESA index is applied in evaluating the driver workload of manually driven vehicles in automated vehicle platooning environments. Driving simulation experiments are conducted to collect drivers' responsive behavior data which are used for exploring the relationship between the NASA-TLX score and the ESA by the correlation analysis. As a result, ESA is found to have the greatest correlation with the NASA-TLX score among the various driver workload evaluation indexes in the lane change scenario, confirming the usefulness of ESA.

• Science of Emotion and Sensibility
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• v.26 no.4
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• pp.51-70
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• 2023

This study employed driving simulation to examine how takeover request (TOR) information modalities (visual, auditory, and visual + auditory) in Level-3 automated vehicles, and road types (straight and curved) influence the driver's control takeover time (TOT) and mental workload, assessed through subjective workload and heart rate variations. The findings reveal several key points. First, visual TOR resulted in the quickest TOT, while auditory TOR led to the longest. Second, TOT was considerably slower on curved roads compared to straight roads, with the greatest difference observed under the auditory TOR condition. Third, the auditory TOR condition generally induced lower subjective workload and heart rate variability than the visual or visual + auditory conditions. Finally, significant heart rate changes were predominantly observed in curved road conditions. These outcomes indicate that TOT and mental workload levels in drivers are influenced by both the TOR modality and road geometry. Notably, a faster TOT is associated with increased mental workload.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.101-111
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• 2022

Conditional autonomous vehicles should hand over control to the driver according on driving situations. However, if the driver is immersed in a non-driving task, the driver may not be able to make suitable decisions. Previous studies have confirmed that the cues enhance take-over performance with a directional information on driving. However, studies on the effect of take-over cues on the driver's brain activities are rigorously investigated yet. Therefore, this study we evaluates the driver's brain activity according to the take-over cue. A total of 25 participants evaluated the take-over performance using a driving simulator. Brain activity was evaluated by functional near-infrared spectroscopy, which measures brain activity through changes in oxidized hemoglobin concentration in the blood. It evaluates the activation of the prefrontal cortex (PFC) in the brain region. As a result, it was confirmed that the driver's PFC was activated in the presence of the cue so that the driver could stably control the vehicle. Since this study results confirmed that the effect of the cue on the driver's brain activity, and it is expected to contribute to the study of take-over performance on biomakers in conditional autonomous driving in future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.266-283
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• 2022

With the rapid development of autonomous vehicle technology, unexpected accidents are occurring. Therefore, it is necessary to minimize user accident damage through the development of autonomous traffic safety education. Since edge cases, accident type, and risk factor analysis are important for realistic education, overseas case studies and demonstrations were carried out, and based on this, two curriculum for service providers and general users were developed. The service provider curriculum consisted of OEDR, sudden stop, cut-in, take-over, defensive driving, system malfunction, policy and information security education, and the general user curriculum consisted of attention duty, take-over, operating design domain, accidents type, laws, functions, information security education.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.07a
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• pp.17-18
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• 2017

본 논문은 스마트 팩토리의 중요 요소인 무인반송차(AGV)를 자율 주행시키기 위해 오픈 소스 자율 주행차 시뮬레이터인 udacity를 이용해 머신 러닝시키는 시스템을 개발하였다. 공장의 운행 루트를 자율주행 시뮬레이터의 전경으로 가공하고, 3개의 카메라를 부착시킨 AGV를 운행시키면서 머신 러닝시킨다. AGV를 주행하여 얻어진 여러 학습 데이터를 통해 도출된 결과들을 각각 비교하여 우수한 모델을 선정하고 운행시킨 결과 AGV가 정해진 운행 루트를 정확하게 주행하는 것을 확인하였다. 이를 통해, 가상 운행 환경에서 저비용으로 AGV 운행 학습이 가능하다는 것을 보였다.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.209-218
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• 2022

Today, with the development of the Internet and mobile technology, consumers' purchasing patterns have shifted from offline to online. In addition, due to the recent COVID-19, online purchases have significantly increased, and accordingly, the courier industry for logistics delivery has also grown significantly. Various logistics robots are being operated in many industrial and can reduce the labor intensity and physical and mental fatigue of workers. However, if the logistics robot does not properly recognize the people or environment around it, it can lead to a serious accident. We conducted that how logistics robots can perform safe work in a working environment such as a logistics warehouse through the application of ISO/DIS 21448 (SOTIF) to autonomous logistics transport robots. This result is expected to contribute to the operation of unmanned logistics warehouses using AGV.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.167-182
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• 2022

The accident data attributes of the traffic accident report are used not only in traditional traffic safety-related research to identify the cause of traffic accidents, but also as basis data for the development of the automated vehicle driving performance verification scenarios. However, since the data attributes of the traffic accident report are limited for the purpose of reconstructing the traffic situation and developing scenarios, this study aims to provide the directions for improvement of traffic accident report, ultimately for its expanded usability for the automated vehicle test scenarios. The directions for improvement of the traffic accident report are provided by categorizing the traffic situation before the accident (pre-crash), the situation immediately before or during the accident (on-crash), and the situation after the accident (post-crash), respectively. Additional data items or data processing methods are presented. Furthermore, data elements that can be extracted from the traffic accident process data in the unstructured narrative form are explored and provided.