• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.196-196
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• 2000

This paper describes a lateral guidance system of an autonomous vehicle, using a neural network model of magneto-resistive sensor and magnetic fields. The model equation was compared with experimental sensing data. We found that the experimental result has a negligible difference from the modeling equation result. We verified that the modeling equation can be used in simulations. As the neural network controller acquires magnetic field values(B$\_$x/, B$\_$y/, B$\_$z/) from the three-axis, the controller outputs a steering angle. The controller uses the back-propagation algorithms of neural network. The learning pattern acquisition was obtained using computer simulation, which is more exact than human driving. The simulation program was developed in order to verify the acquisition of the teaming pattern, teaming itself, and the adequacy of the design controller. The performance of the controller can be verified through simulation. The real autonomous electric vehicle using neural network controller verified good results.

• ETRI Journal
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• 제37권3호
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• pp.617-625
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• 2015

In this paper, a steering control system for the path tracking of autonomous vehicles is described. The steering control system consists of a path tracker and primitive driver. The path tracker generates the desired steering angle by using the look-ahead distance, vehicle heading, and a lateral offset. A method for applying an autonomous vehicle to path tracking is an advanced pure pursuit method that can reduce cutting corners, which is a weakness of the pure pursuit method. The steering controller controls the steering actuator to follow the desired steering angle. A servo motor is installed to control the steering handle, and it can transmit the steering force using a belt and pulley. We designed a steering controller that is applied to a proportional integral differential controller. However, because of a dead band, the path tracking performance and stability of autonomous vehicles are reduced. To overcome the dead band, a dead band compensator was developed. As a result of the compensator, the path tracking performance and stability are improved.

• 시스템엔지니어링학술지
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• 제18권2호
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• pp.40-49
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• 2022

Remote-controlled and autonomous driving based on artificial intelligence are key elements required for unmanned combat vehicles. The required capability of such an unmanned combat vehicle should be expressed in reasonable required operational capability(ROC). To this end, in this paper, the requirements of an unmanned combat vehicle operated under a manned-unmanned teaming were analyzed. The functional requirements are remote operation and control, communication, sensor-based situational awareness, field environment recognition, autonomous return, vehicle tracking, collision prevention, fault diagnosis, and simultaneous localization and mapping. Remote-controlled and autonomous driving of unmanned combat vehicles could be achieved through the combination of these functional requirements. It is expected that the requirement analysis results presented in this study will be utilized to satisfy the military operational concept and provide reasonable technical indicators in the system development stage.

• 자동차안전학회지
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• 제16권2호
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• pp.14-19
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• 2024

This paper describes autonomous emergency braking systems (AEB) for elderly drivers designed to consider their driving characteristics. With aging, perception-reaction time, and decision-making time increase accordingly. Without being aware of these performance degradations, however, changes in driving patterns due to increased alertness while driving lead to vehicle crashes. Therefore, it is necessary to develop an autonomous emergency braking system by incorporating the characteristics of the elderly driver. In order to enhance the driver acceptance of older people, perception-reaction time, alertness, and ride comfort need to be considered for conventional autonomous emergency braking systems (C-AEB). Proactive AEB(P-AEB) algorithm has been proposed to reflect human factor of elderly driver above. The performance of the proposed algorithm has been evaluated through MATLAB simulink simulation studies. It has been shown from the computer simulations that the proposed P-AEB algorithm enhances the driver acceptance of older people by improving ride comfort while ensuring safety of vehicle.

• 자동차안전학회지
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• 제16권1호
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• pp.29-34
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• 2024

This paper presents a multi-label lane detection method for autonomous vehicles based on deep learning. The proposed algorithm can detect two types of lanes: center lane and normal lane. The algorithm uses a convolution neural network with an encoder-decoder architecture to extract features from input images and produce a multi-label heatmap for predicting lane's label. This architecture has the potential to detect more diverse types of lanes in that it can add the number of labels by extending the heatmap's dimension. The proposed algorithm was tested on an OpenLane dataset and achieved 85 Frames Per Second (FPS) in end to-end inference time. The results demonstrate the usability and computational efficiency of the proposed algorithm for the lane detection in autonomous vehicles.

• 한국ITS학회 논문지
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• 제18권2호
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• pp.114-128
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• 2019

최근 전 세계적으로 자율주행자동차의 관심이 높이지고 있으며, 이에 따라 자율주행자동차의 안전성도 중요한 화두로 대두되고 있다. 국내에서도 자율주행자동차 실험도시인 K-City 구축 등 자율주행자동차 개발이 활발해짐에 따라, 자율주행자동차의 안전성을 평가할 수 있는 시나리오 개발이 필요한 시점이다. 본 연구에서는 경찰청 교통사고 데이터와 텍스트 마이닝 기법을 이용하여 K-City 커뮤니티부 도로 자율주행자동차 실험 시나리오를 개발하였다. 개발 결과, K-City 커뮤니티부 도로에서 자율주행자동차 실험을 위한 총 24개의 시나리오가 개발되었다. 또한, 페가수스 5-레이어 모형을 적용한 Logical 및 Concrete 혼합 시나리오 양식을 최종적으로 도출하였다.

• 한국전자거래학회지
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• 제24권2호
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• pp.91-112
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• 2019

5세대 통신기술(5G)의 등장에 따라 4차 산업혁명 관련 첨단기술과 융합한 다양한 통신 서비스가 나타나고 있다. 그 중, 5G 기술의 효과적 실용적 활용을 위해 본 논문에서는 IT 전문가들을 대상으로 설문을 실시하였고, 그 결과 5G 기술과 융합하여 가장 많은 서비스 활성화를 이끌 수 있는 4차 산업혁명 분야로서 자율주행차가 선정되었다. 이를 기반으로, 5G 기반 자율주행차 활용 산업 및 서비스 분류체계를 개발함으로써 자율주행차를 활용한 신사업 개발 및 새로운 비즈니스 모델개발을 지원하는 기틀을 제공하였다. 이와 더불어, 개발된 새로운 자율주행자동차 산업-서비스 분류체계를 통해 연계 Matrix를 작성하여 향후 차세대 자율 주행차가 활용될 수 있는 분야에 대한 실용적인 산업-서비스 개발을 위한 가이드라인을 제공하고자 한다.

• 융합보안논문지
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• 제18권4호
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• pp.53-61
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• 2018

최근 4차 산업혁명이 화두로 등장하면서 자율주행자동차에 대한 관심과 함께 상용화를 위한 연구개발이 활발히 이루어지면서 자율주행자동차의 중요성과 관심이 높아지고 있으며, 미국, 영국, 독일을 포함한 여러 국가들은 이러한 추세를 반영하여 자율주행자동차의 상용화에 따른 법제들을 마련하거나 기존 법률을 개정하고 있는 상황이다. 국내의 경우도 자율주행자동차의 상용화를 위한 임시운행과 연구개발이 이루어지고 있으나 자율주행자동차 상용화에 따른 법제들은 미비한 상황이다. 본고에서는 자율주행자동차 법률 체계에 기반 하여 미국, 영국, 독일의 법제 현황과 국내 법제 현황을 비교하여 자율주행자동차 상용화를 위한 법제와 정책과제를 제시하여 자율주행자동차 법률 체계 완성을 위한 단계별 추진방향을 제시한다. 또한 자율주행자동차 상용화에 따른 산업 발전을 위한 법제 정비 방향에 대한 방안을 제시한다.

• Journal of Biosystems Engineering
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• 제25권2호
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• pp.131-140
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• 2000

Spraying operation is one of the most essential in an orchard management and it is also hazardous to human body. for automatic and unmanned spraying , an autonomous travelling vehicle is demanded. In this study, a telemeter was developed using infrared beam which could detect trunks and obstacles measure distance and direction from the vehicle travelling in the orchard. The telemeter system was composed of two infrared LED transmitters and receivers, a beam scanning device for continuous object detection , two rotary encoders for angle detector, and a beam level controller for uneven soil surface. The detected distance and direction signal s were sent to personal computer which made for the system display the angular and distance measurements through I/O board. According to a field test in an apple farm, the system detected up to 10m distance under 12 V of transmitted beam intensity, however, it was recommended that the proper beam transmit intensity be 7 v at the 10 m distance, because of the negative effect to human body at 12 V. The error rate of this system was 0.92 % when the actual distance was compared to measured one. The system was feasible at the small error rate. The developed telemeter system was an important part for autonomous travelling vehicle provided the real time object recognition . A direction control system could be constructed suing the system. It is expected that the system could greatly contribute to the development of autonomous farm vehicle.

• 제어로봇시스템학회논문지
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• 제22권10호
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• pp.803-810
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• 2016

This paper presents a study of localization methods based on particle filter using 2D laser sensor measurements and road feature map information, for autonomous vehicles. In order to navigate in an urban environment, an autonomous vehicle should be able to estimate the location of the ego-vehicle with reasonable accuracy. In this study, road features such as curbs and road markings are detected to construct a grid-based feature map using 2D laser range finder measurements. Then, we describe a particle filter-based method for accurate positional estimation of the autonomous vehicle in real-time. Finally, the performance of the proposed method is verified through real road driving experiments, in comparison with accurate DGPS data as a reference.