• Broadcasting and Media Magazine
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• v.24 no.1
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• pp.63-72
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• 2019

고령인구의 증대 및 출생 인구의 감소로 과소지와 대중교통 취약지역이 확산되고 있다. 이러한 인구구조의 변화는 교통약자의 독립적 이동이 더욱 불편해지고, 물류의 수송이 어려워 진다. 한편, 도심은 차량의 포화상태로 대기환경의 질이 나빠지고 도심도로는 주차장으로 변질될 뿐만 아니라, 운전자와 보행자 모두에게 안전의 위협이 되고 있다. 이러한 환경과 이동의 효율성을 극대화 하는 방안으로 친환경자동차와 자율주행기술의 접목이 연구개발 중이다. 자율주행기술은 자율주행차와 도로 인프라에 ICT가 융 복합되어 이동과 수송분야의 새로운 산업과 서비스 창출이 가능하다. 본 고에서는 교통약자의 이동과 물류의 수송을 지원하는 자율주행기술의 개발 동향을 살펴본다. 특히 광화문, K-City 등의 도심 자율주행서비스 테스트 경험을 통해 해결해야 하는 복잡한 도심 주행 환경의 인지와 교차로 및 합류로, 비정형 도로환경에서의 주행협상기술의 필요성을 소개한다. 도심의 주행 환경은 고속도로와 같은 자동차 전용도로와 달리, 신호등과 교차로, 2륜 이동체 및 버스 등이 다양하게 혼재된 것으로 인지 및 판단 기능의 고도화가 적극적으로 요구된다. 그리고, 다양한 자율주행서비스 시장을 확산하기 위해 요구되는 이동하는 공간과 시간을 메꿔 줄 미디어 콘텐츠의 역할에 대해 설명하고자 한다.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.621-622
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• 2019

본 논문은 도심 환경하에서 운행 가능한 자율주행차량 시스템의 구현 방안에 대해 다루고 있다. 현대자동차가 주최한 2019 대학생 자율주행 경진대회는 도심 환경을 재현한 K-City 에서 열렸고, 도심 환경에서 발생할 수 있는 돌발 장애물 인지, 공사 구간 우회, 교차로 신호등 인지, 사고 차량 회피, 응급 차량에게 차선 양보 및 톨게이트 통과 등의 6 개의 미션을 자율주행차량이 무인운전으로 수행하는 것이었다. 이 대회를 위해 본 연구팀에서 개발한 자율주행 시스템은 리웍된 실제 차량에 탑재되어 대회장의 모든 주행 미션을 성공적으로 수행하였다.

• 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.

• The Journal of the Korea Contents Association
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• v.19 no.12
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• pp.452-459
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• 2019

In order for an autonomous vehicle to be commercialized, it is necessary to conduct a safety test for every aspect. Considering the implementation of the autonomous vehicles technologies to the highest level, it is necessary to analyze the possible scenarios in the most complex environment as in the urban area. It should be confirmed whether autonomous vehicles can be operated with conventional traffic signal environment. It is also required to confirm the ability of autonomous vehicles in interacting with other vehicles, and coping with possible accidents on the road. In this study, the evaluation factors of autonomous vehicles in the road environment are selected by referring to the other evaluation protocols like ADAS. Study result would be reflected in establishing the autonomous vehicle evaluation method for different test environment along with various technology implementation level.

• Journal of Auto-vehicle Safety Association
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• v.15 no.2
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• pp.21-27
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• 2023

This paper presents a simulation tool for developing and evaluating automated driving systems' lane change algorithm in urban congested traffic. The behavior of surrounding vehicles was modeled based on driver driving data measured in urban congested traffic. Surrounding vehicles are divided into aggressive vehicles and non-aggressive vehicles. The degree of aggressiveness is determined according to the lateral position to initiate interaction with the vehicle in the next lane. In addition, the desired velocity and desired time gap of each vehicle are all randomly assigned. The simulation was conducted by reflecting the cognitive limitations and control performance of the autonomous vehicle. It was possible to confirm the change in the lane change performance according to the variation of the lane change decision algorithm.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.222-234
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• 2020

Autonomous vehicles are equipped with a wide rage of sensors such as GPS, RADAR, LIDAR, camera, IMU, etc. and are driven by recognizing and judging various transportation systems at intersections in the city. The accident ratio of the intersection of the autonomous vehicles is 88% of all accidents due to the limitation of prediction and judgment of an area outside the sensing distance. Not only research on non-signalized intersection collision avoidance strategies through V2V and V2I is underway, but also research on safe intersection driving in failure situations is underway, but verification and fragments through simple intersection scenarios Only typical V2V failures are presented. In this paper, we analyzed the architecture of the V2V module, analyzed the causal factors for each V2V module, and defined the failure mode. We presented intersection scenarios for various road conditions and traffic volumes. we used the ISO-26262 Part3 Process and performed HARA (Hazard Analysis and Risk Assessment) to analyze the risk of autonomous vehicle based on the simulation. We presented ASIL, which is the result of risk analysis, proposed a monitoring concept for each component of the V2V module, and presented monitoring coverage.

• 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.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1095-1109
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• 2021

The technology used to recognize the location and surroundings of autonomous vehicles is called SLAM. SLAM standsfor Simultaneously Localization and Mapping and hasrecently been actively utilized in research on autonomous vehicles,starting with robotic research. Expensive GPS, INS, LiDAR, RADAR, and Wheel Odometry allow precise magnetic positioning and mapping in centimeters. However, if it can secure similar accuracy as using cheaper Cameras and GPS data, it will contribute to advancing the era of autonomous driving. In this paper, we present a method for converging monocular camera with RTK-enabled GPS data to perform RMSE 33.7 cm localization and mapping on the urban road.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.109-123
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• 2024

Research into vision-based end-to-end autonomous driving systems utilizing deep learning and reinforcement learning has been steadily increasing. These systems typically encode continuous and high-dimensional vehicle states, such as location, velocity, orientation, and sensor data, into latent features, which are then decoded into a vehicular control policy. The complexity of urban driving environments necessitates the use of state representation learning through networks like Variational Autoencoders (VAEs) or Convolutional Neural Networks (CNNs). This paper analyzes the impact of different image state encoding methods on reinforcement learning performance in autonomous driving. Experiments were conducted in the CARLA simulator using RGB images and semantically segmented images captured by the vehicle's front camera. These images were encoded using VAE and Vision Transformer (ViT) networks. The study examines how these networks influence the agents' learning outcomes and experimentally demonstrates the role of each state representation technique in enhancing the learning efficiency and decision- making capabilities of autonomous driving systems.