• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.162-176
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• 2021

Recently, Autonomous Vehicle(AV) has been expected to solve various transportation problems. s the problem of environmental pollution become serious, research to reduce pollution is needed. However, empirical research on AV related pollution is insufficient. Based on this background, this study analyzed network performance changes and CO₂ emissions introduc AVs and Electric Vehicles(EV) in eight intersections. The results show that when AVs with internal combustion engines were, the effect of carbon reduction over the network was insignificant. On the other hand, it was that the total amount of CO₂ generated in the network decreased significantly when EVs and autonomous electric vehicles were emissions in the transportation sector.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.3
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• pp.97-106
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• 2024

The Data Storage System for Automated Driving(DSSAD) is a system that records driving information of Lv.4 or higher autonomous vehicles and is different from EDR that records car information in emergency situations. The study of DSSAD recordings is important for responding to various events that may occur in the future commercialization of Lv.4 autonomous vehicles. Therefore, in this study, we conducted a expressway automated driving demonstration and analyzed the collected data to derive the recording elements of DSSAD. During our two-year demonstration of autonomous driving on expressways, we collected and analyzed instances of disengagement. Our findings indicate that 51.6% of disengagement on expressways occurred during lane changes. From the study, we have identified DSSAD record elements for analyzing disengagement situations. Furthermore, implications of future research direction of disengagement analysis were presented.

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.2
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• pp.135-148
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• 2020

The ongoing development of autonomous driving-related technology may cause different kinds of accidents while testing new changes. As a result, more information on ODD suitable for the domestic road environment will be necessary to prevent safety accidents. Besides, implementation of the Autonomous Vehicle Act will increase autonomous driving demonstrations on roads currently in use. This study describes an ODD for demonstrating an autonomous driving shuttle in downtown areas. It addresses a possible scenario of autonomous driving around a downtown road in Anyang. Geometric, operational, and environmental factors are considered while maintaining a domestic road environment and safety. Autonomous driving shuttles are demonstrated in 30 nodes, each identified by node type and signal-communication. Link criteria are an autonomous driving restriction in 42 morning peak (8-9am) hours, 39 non-peak (12-13pm) hours, and 40 afternoon peak (18-19pm) hours. In the future, conclusions may be considered for preliminary safety assessments of roads where autonomous driving tests are performed.

• Journal of Venture Innovation
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• v.5 no.4
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• pp.91-108
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• 2022

This study is an empirical analysis regarding what kind of factors affect the intention to use autonomous vehicles. For the empirical analysis the research model was derived from value-based adoption model base and integrated some aspects that only autonomous vehicles have. At default variables of VAM are usefulness, enjoyment, technicality, perceived cost, some autonomous vehicle related variables were added, and those are convenience, safety, security, social influence. A survey was done in order to empirically analyze with this research model, and 216 valid survey answers were chosen to analyze. Empirical analysis was done by structural equation using AMOS24. The result of empirical analysis were as follows. Variables usefulness, enjoyment, safety, security had a significant positive effect on perceived value. Technicality and perceived cost had a significant negative effect of perceived value. In addition, security and social influence had no significant effect on perceived value. Furthermore, perceived value had significant positive effect on intention to use. Among the variables that came out to be significantly positive, the most influencing variable was safety, followed by convenience, perceived cost, enjoyment, usefulness and then technicality. In addition, the analysis of mediating effect of perceived value shows that usefulness, enjoyment, convenience, safety, technicality, perceived cost had mediating role towards intention to use. However, security and social influence had no siginificant mediating effect towards intention to use. Considering all these research results this study has provided theoretical and practical implications to researchers on the intention to use autonomous vehicles.

• Journal of Technology Innovation
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• v.30 no.2
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• pp.1-27
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• 2022

In recent years, the autonomous vehicle industry has changed drastically. So the needs and interests in predicting future technologies and market prospects of the autonomous vehicle field have been very increased. However, considering the characteristics of the automotive industry, which has various factors, complex correlation of them and big influence on each other, the study of systematic future forecasting methodologies are urgent and necessary which are applicable to autonomous vehicle industry. In this research, the two methods such as "Field Anomaly Relaxation" and "Multiple Perspective Concept" were analyzed and chosen, which are suitable to automotive industry. By the combination of two methods this research developed and examined the three future scenarios related to core technologies and industry trends. And these scenarios feasibility was verified by experts and evaluation checklist. This research has a contribution that this future scenario forecasting approach can be applied to the industries which have various volatility like the autonomous vehicle industry.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.1
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• pp.1-15
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• 2022

In order to trust and use autonomous vehicles, safe driving on the road must be guaranteed. For this, the first infrastructure to be equipped with autonomous driving is traffic safety facility. On the other hand, autonomous vehicles(Level 3) and general vehicles are driving on the road, it is necessary to additionally manage existing general traffic safety facilities. In this study, a field management system for traffic safety facilities based on autonomous driving infrastructure was studied, and a pilot field management system was implemented in the demonstration area(Pangyo). The pilot system consists of a GNSS(Global Navigation Satellite System) receiver, a field management equipment, and a field management app. As a result of field demonstration,, it was confirmed that traffic safety facility information was easily transmitted and received even in downtown areas and that could be efficiently operated and managed. It is expected that the results of this study will be used as reference materials for the spread of autonomous driving infrastructure to local governments and infrastructure construction in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.163-181
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• 2023

Improving the detection performance of facilities corresponding to the sensors of autonomous vehicles helps driving safety. In the road and transportation field, research is being conducted to improve the detection performance of sensors by road infrastructure or facilities. As part of this on the development of autonomous driving support infrastructure, the shape of traffic cones and drums to ensure sufficient LiDAR detection performance even rainy conditions and maintain the line-of-sight guidance function in construction zones improvement effect. The principle was to increase reflection performance and ensure no significant difference in shape from existing facilities. Traffic cones were manufactured in square pyramid shapes instead of cones, and drums were manufactured in hexagonal and octagonal pillar shapes instead of cylinders. LiDAR detection data for the facility was confirmed on a clear day and with 20 mm/h and 40 mm/h rainfall. The detection performance of the square pyramid-shaped traffic cone and octagonal column-shaped drum was to the existing facility. On the other hand, deviations occurred due to repeated measurements, and significance could not be confirmed through statistical analysis. By reflecting these results, future studies will seek a form in which data can be obtained uniformly despite the diversity of measurement environments.

• Broadcasting and Media Magazine
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• v.24 no.1
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• pp.41-48
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• 2019

최근 유럽 위원회(European Commission)의 ICT 분야 연구에 대한 목표는 사이버 보안, 사생활 보호, 5G, 사물의 인터넷, 데이터 경제, 자유로운 데이터의 흐름(free flow of data) 등과 같은 디지털 정책 분야에 미래 EU(European Union) 자율주행 프로젝트의 초점을 맞추는 것이다. 이를 위하여 2017년과 2018년, 10개의 디지털 국경 커리도(Digital Cross-Border Corridors)가 발표되었고, 5G 기반 CCAM 서비스 실증을 확립하였다. 유럽 위원회는 이러한 5G 기반 CCAM 서비스 실증의 일환으로 2018년 3개의 대규모 프로젝트(5G-MOBIX, 5G-CARMEN, 5GCroCo)를 선정하였다. 본 기고문에서는 2018년 유럽 위원회에서 선정한 3개의 5G 기반 CCAM 프로젝트에 대하여 설명한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.111-116
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• 2021

This study is a study on improving the pedestrian Care system, which delivers jaywalking events in real time to the autonomous driving control center and Autonomous driving vehicles in operation and issues warnings and announcements to pedestrians based on pedestrian signals. In order to secure reliability of object detection method of pedestrian Care system, the inspection method combined with camera sensor with Lidar sensor and the improved system algorithm were presented. In addition, for the occurrence events of Lidar sensors and intelligent CCTV received during the operation of autonomous driving vehicles, the system algorithm for the elimination of overlapping events and the improvement of accuracy of the same time, place, and object was presented.