• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.153-170
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• 2020

With the recent development of AI and ICT, autonomous vehicles are becoming a reality, and sales of the vehicles equipped with partial autonomous driving technology are also rapidly expanding. In this situation, technology research on autonomous vehicles has been actively conducted, but research on exploring the perception of autonomous vehicles from the user's perspective is relatively insufficient. Therefore, this study categorizes autonomous vehicle users into four types - , , , and . Then, it examines the characteristics of each type. For this purpose, we applied Q-methodology, a qualitative research method, to observe self-referent subjectivity of 32 P-samples using a Q-sample which consists of 34 statements. The results of our study have significance in that they provide domestic and global automakers with strategic directions for technological development and market expansion of autonomous vehicles, and academically provide hypotheses for subsequent quantitative research.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.579-585
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• 2008

Recently, many researches on autonomous mobile system have been proposed, which are possible to recognize its surrounding environment and navigate to destination without supervisor's intervention. Various sensors are mounted on the autonomous systems in order for the systems to move to destination safely without any accident. In this paper, we design an ontology-based autonomous system mounted laser distance sensors and cameras, and propose a method to conceptualize sensing information. We show the validity of the proposed method through the experiments of the system's navigation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.4
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• pp.62-77
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• 2022

A cooperative driving automation system is imperative to overcome the limitation of the stand-alone automated driving technology. By definition, a cooperative driving automation system refers to a technology in which an automated vehicle cooperates with other vehicles or infrastructure to increase driving efficiency and safety. Specifically, in this study, the technical elements necessary for the cooperative driving automation technology and the technological research trends were investigated. Subsequently, implications for future cooperative driving automation technology development were drawn through the research trends. Finally, the importance of cooperative driving automation technology and infra-guidance service for automated vehicles were discussed.

• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.253-259
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• 2018

The Korea Convergence Society. Recently, as the 4th industrial revolution is rising, there are many changes in various field of industries. Among these industries, autonomous vehicles based on artificial intelligence, big data and internet of things is one of the most promising industry. Autonomous vehicle stray from classical car domain of manufacturers and suppliers, IT/Electronics suppliers and communication companies are widen their business area to autonomous vehicle technology. In this paper, we analysis the state of art of autonomous vehicle technology and development direction of industries/research institute. Finally, we discuss the social/economic effects of autonomous vehicle.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.93-102
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• 2023

Data processing through the cloud causes many problems, such as latency and increased communication costs in the communication process. Therefore, many researchers study edge computing in the IoT, and autonomous driving is a representative application. In indoor self-driving, unlike outdoor, GPS and traffic information cannot be used, so the surrounding environment must be recognized using sensors. An efficient autonomous driving system is required because it is a mobile environment with resource constraints. This paper proposes a machine-learning method using neural networks for autonomous driving in an indoor environment. The neural network model predicts the most appropriate driving command for the current location based on the distance data measured by the LiDAR sensor. We designed six learning models to evaluate according to the number of input data of the proposed neural networks. In addition, we made an autonomous vehicle based on Raspberry Pi for driving and learning and an indoor driving track produced for collecting data and evaluation. Finally, we compared six neural network models in terms of accuracy, response time, and battery consumption, and the effect of the number of input data on performance was confirmed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2020.11a
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• pp.157-158
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• 2020

항만용 자율주행 야드트럭운행 환경은 무신호교차로 주행, 낮은 GPS정확도, 악천후상황주행, 이송 컨테이너 위치변경등과 같이 일반 도로의 센서기반의 자율주행차량 운행과 다르게 매우 복잡하다. 이를 위해서는 항만내 특성을 반영한 실시간 위치, 속도 등에 대한 정확한 인식이 중요한 요소이다. 이를 위해서 센서융합과 V2X기반의 복합적인 항만용 실시간 로컬 동적지도 (Local Dynamic Map) 생성 및 V2X기반의 협력측위를 통하여서 기존의 독립적인 자율주행차량의 위치 인식보다 더 개선된 고정밀 위치 인식 정보추출이 필요하다. 본 연구에서는 복합적인 항만용 동적지도 생성관리시스템의 설계 방안 및 협력측위 기술 적용 방안을 제시하고 이를 활용한 항만 구역내 자율주행차량 및 모든 화물 이송장비들의 실시간 위치 인식뿐만 아니라 이동체의 사전 충돌예측 및 비상정지 안전 제어 가능한 V2X 기반의 인텔리젼스 한 3차원 관제시스템 설계 방안을 제시하고자 한다.

• 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.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.298-298
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• 2022

최최근 자율주행차의 안전한 주행을 보장하기 위한 모델 공식 기반 접근이 제시되고 있으며, 인텔-모빌아이의 RSS(responsibility-sensitive safety) 모델[1, 2]과 엔비디아의 SFF(Safety Force Field) 방법[2]이 주목받고 있다. 자율주행차 시뮬레이터을 이용하여 이러한 안전 주행 모델 적용의 효과와 역효과를 시뮬레이션하는 것은 자율주행 자동차와 제도의 개발에 매우 중요하다. 본 연구에서는 RSS와 SFF 모델을 살펴보고 이를 자율주행에 적용하기 위한 비교 연구 방법을 제안하고자 한다.

• 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.

• Information and Communications Magazine
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• v.33 no.4
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• pp.41-46
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• 2016

본 고에서는 협력 자율 주행을 위한 V2X 통신 기술을 소개한다. 자동차는 IT 기술이 접목되어 편리하고 안전하며 지능화된 자동차로 발전하고 있으며 2020년에는 자율주행자동차가 출현할 것으로 전망하고 있다. V2X 통신 기술은 자동차 안전 지원 서비스를 제공하며 다양한 차량 센서와 융합되어 주행 제어가 이루어지는 협력 자율 주행 서비스도 제공할 수 있다. 본 고에서는 차량 안전 기술과 협력 자율 주행 기술의 개념과 V2X 통신 기술 개발 동향 및 표준화 동향, 그리고 향후 전망에 대하여 살펴 본다.