• ICROS
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• v.18 no.2
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• pp.21-29
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• 2012

최근 자동차 연구개발에 있어 내연기관을 대체하는 친환경 자동차기술과 더불어 무인자동차, 자율주행기술이 많은 부분에서 시도되고 실현되어가고 있다. 지능형 자동차라는 개념에서 적용되었던 운전자안전보조시스템, 편의지원과 사고경감 시스템 등이 하나로 통합되어 무인자동차 기술로 발전하고 있다. 또 차량에 고가의 센서를 장착하여 주변환경이나 운전자를 모니터링하는 방식에서 IT 융합기술을 이용한 네트워크기술 (V2I, V2V, V2N & V2X)을 접목시키는 방안을 통하여 개개의 차량은 물론 교통체계의 전체적인 변화를 추구하고 있다. 이러한 첨단차량기술은 새로운 교통문화(차량공유시스템, 군집주행)의 개발과 또다른 교통체계의 연구로 확장되어가고 있다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.237-242
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• 2019

Recently, as the public interest in autonomous cars has increased, research and technology development of related companies for the commercialization of autonomous cars have been actively carried out, and the development has progressed to a stage where they are partially but actually used. However, in March 2018, Uber and Tesla cars caused two fatal accidents, and the need for a new system is emerging. Therefore, this paper suggests a new information processing model for autonomous driving car system by supplementing the cause of recognition errors caused by the cause of death by focusing on the accident of autonomous driving car.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.251-261
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• 2019

The importance of autonomous driving systems that utilize V2X services such as V2V(Vehicle to Vehicle) and V2I(Vehicle to Infrastructure) for safer and more comfortable driving is increasing with the recent development of autonomous vehicles. Partly autonomous vehicles based on environmental sensors have limitations for predicting and determining areas beyond the recognition distance of the mounted sensors and in response to atypical objects that are difficult to detect. Therefore, it is important to utilize the V2X service to improve the limit of sensor detection performance and to make driving safer and more comfortable. However, there may be an accident risk of autonomous vehicles due to incorrect information provided by V2X. Thus, the application of technology to prevent this needs to be considered. In this pater, we used the ISO-26262 Part3 Process and performed HARA (Hazard Analysis and Risk Assessment) to derive the risk sources of autonomous vehicles due to V2I malfunctions by using the communication between vehicles and infrastructure among V2X. We also developed ASIL ratings based on the simulations and real vehicle tests of the malfunctions of major cases of usnig V2I.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.325-328
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• 2019

자율주행 자동차는 운전자가 개입하지 않고 차량에 부착된 다양한 센서를 통해 얻은 데이터를 기반으로 차량을 스스로 제어하며 설정한 목적지까지 주행한다. 본 논문에서는 단일 카메라와 영상을 사용한 차선 검출하고 추적하는 방법을 제안한다. 영상의 하단 부분만 분리하여 차선을 검출하기 위하여 외곽선 검출 과정을 거친 후 허프 변환을 통해 양 차선의 중심을 구한다. 이 값을 바탕으로 PID 제어로 차량의 차선을 유지한다. 모형 차량과 모형 트랙에서 차선 인식 후 차선을 추적하여 주행하는 동작을 시험하였다. PID 제어를 위헌 적정 각 항의 값을 구하였다. 시험 결과 차선 검출 알고리즘은 성공적으로 동작함을 확인할 수 있었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.4
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• pp.327-333
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• 2016

An autonomous vehicle is a convergence of artificial intelligence and a vehicle which can drive itself while analyzing the real-time situation on a road without a driver. A lot of research achievements have been revealed through the media and Google is considered to be the best leading company in this field. The use of patent information which contains various information such as bibliographic data and information about technologies is a good way to find out the R&D direction of a company and develop a reasonable strategy. This study is aimed at investigating the direction to which Google focuses its R&D capabilities and establishing strategies for technology development. Google's patents about autonomous vehicles were collected and the degree of research bias was analyzed using Social Network Analysis based on citations indicating the quality of a patent. Based on the results, the strategies for technology development was eventually proposed. As a result, it was revealed that Google focused its R&D capabilities on the part of hardware control to make up for its lack of hardware-oriented technologies. As of now, Google obtained remarkable achievements, so it seems reasonable that last-movers consider cooperative research with Google.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.37-38
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• 2018

자동차 시스템의 전자화로 자동차에서 소프트웨어가 차지하는 비중은 점차 증가하고 있다. 소프트웨어는 안전필수 시스템에서 중요한 위치를 차지하며, 소프트웨어의 오동작은 심각한 손실 또는 재난을 일으키는 원인이 될 수 있다. 최근 자동차 시스템은 자율주행차량을 목표로 하여 센서융합과 협조제어가 많아지고 있다. 센서와 협조제어 정보는 시스템의 자율화에 중요한 부분을 차지하지만 현재 산업구조에서는 관련 부분에 대한 검증이 쉽지 않다. 본 논문에서는 현재 상황에서 발생할 수 있는 현상들을 살펴보고 안전한 자동차 개발을 위해 우리가 나아가야 할 방향을 고찰한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.205-206
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• 2018

Recently, interest in unmanned vehicles, autonomous vehicles and connected cars has increased, and autonomous driving capability is also increasing. Depending on the autonomous driving ability, the maximum number of steps is divided into 6 steps. The higher the step, the less the involvement of the person in the running, and the person does not need to be involved at the highest stage. Today's autonomous vehicles have been developed in stages 4 to 5, but solutions are not clearly defined in case of an accident, so only the test run is possible. Such an accident occurring during traveling is almost inevitable, and it is judged who has made a mistake in case of an accident, which increases the punishment for the accident. Although a black box is used to clarify such a part, it is easy to delete a photographed image, and it is difficult to solve an accident such as a hit-and-run. In this paper, we design a driving record system using black chain to solve such an accident.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.6
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• pp.916-921
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• 2018

Recently, interest in autonomous vehicle has increased, and autonomous driving capability is also increasing. Depending on the autonomous driving ability, the maximum number of steps is divided into 6 steps. The higher the step, the less the involvement of the person in the running, and the person does not need to be involved at the highest stage. Today's autonomous vehicles have been developed high level, but solutions are not clearly defined in case of an accident, so only the test run is possible. Such an accident occurring during traveling is almost inevitable, and it is judged who has made a mistake in case of an accident, which increases the punishment for the accident. Although a black box is used to clarify such a part, it is easy to delete a record, and it is difficult to solve an accident such as a hit-and-run. In this paper, i design a driving record system using black chain to solve accidents.

• Design Convergence Study
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• v.17 no.4
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• pp.17-29
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• 2018

In the semi-autonomous vehicle, before reaching a fully autonomous driving stage, it is imperative for the system to issue a take-over request(TOR) that asks a driver to operate manually in a specific situation. The purpose of this study is to compare whether head-up display(HUD) is a better human-vehicle interaction than head-down display(HUD) in the event of TOR. Upon recognition of TOR in the experiment with a driving simulator, participants were prompted to switch over to manual driving after performing a secondart task, that is, playing a game, while in auto-driving mode. The results show that HUD is superior to HDD in 'ease of use' and 'satisfaction' although there is no significant difference in reaction time and subjective workload. Therefore, designing secondary tasks through HUD during autonomous driving situation improves the user experience of the TOR function. The implication of this study lies in the establishing an empirical case for setting up UX design guidelines for autonomous driving context.

• Journal of Auto-vehicle Safety Association
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• v.13 no.1
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• pp.38-44
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• 2021

This study aimed to verify the criteria of the driver monitoring systems proposed by UNECE ACSF informal working group and the ministry of land, infrastructure, and transport of South Korea using driving behavior data. In order to verify the criteria, we investigated the safety regulations of driver monitoring systems in a conditional autonomous vehicle and found that the driver monitoring measures were related to eye blinks times, head movements, and eye closed duration. Thus, we took two different experimental data including real-world driving and simulator-based drowsy driving behaviors in previous studies. The real-world driving data were used for analyzing blink times and head movement intervals, and the drowsiness data were used for eye closed duration. In the real-world driving study, 52 drivers drove approximately 11.0 km of rural road (about 20 min), 7.9 km of urban road (about 25 min), and 20.8 km of highway (about 20 min). The results suggested that the appropriate number of blinks during the last 60 seconds was 4 times, and the head movement interval was 35 seconds. The results from drowsy driving data will be presented in another paper - part 2.