• 한국콘텐츠학회논문지
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• 제21권12호
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• pp.705-712
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• 2021

본 연구는 2027년 적용 가능한 레벨5의 자율주행차량을 기반으로 자율주행 DRT UX 디자인 특성을 제안하는데 목적이 있다. 연구범위로는 고령자와 차량 내외의 인터랙션을 중심으로 한 시스템으로 하였으며, 레벨5의 자율주행차량을 연구대상으로 하였다. 적용 대상으로는 2021년을 기준으로 60대부터 90대로 설정하였다. 본 연구는 고령자와의 직접적인 소통을 통한 실제적인 인사이트를 도출한 자율주행 DRT UX 디자인 특성 연구라는데 기존 연구와의 차별성이 있다. 연구 방법으로는 문헌연구를 통해 자율주행차량과 DRT를 이론적으로 고찰하였으며, 이를 바탕으로 자율주행차량과 DRT의 사례를 분석하였다. 사례연구로는 고령자 인터뷰, 자율주행차량 시승, 영상제작, 설문조사, 고령자 자율주행차량 VR 시승을 통한 일반화로 진행하였다. 포커스 그룹 인터뷰(FGI)를 통해 자율주행 DRT UX 디자인 특성 10가지를 도출하였으며, 도출된 특성은 예약, 승차, 입력, 주행, 응급, 하차 등으로 구분되었다. 본 연구를 통해 고령자의 이동성 향상에 이바지하고 자율주행 DRT의 실용화를 한층 앞당기는 계기가 되기를 바란다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1126-1130
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• 2004

This paper describes the design, development, and performance testing of an autonomous ground vehicle that was developed to participate in the DARPA Grand Challenge that was held in March 2004. The authors of this paper are members of Team CIMAR which was one of twenty five teams selected by DARPA to participate in a competition to develop an autonomous vehicle that can navigate from near Los Angeles to near Las Vegas at speeds averaging twenty miles per hour. Most of the event was held on open terrain and trails in a rocky desert environment. This paper describes the overall system design and the performance of the system at the event.

• 제어로봇시스템학회논문지
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• 제18권3호
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• pp.251-257
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• 2012

Unmanned ground vehicles have important military, reconnaissance, and materials handling application. Many of these applications require the UGVs to move at high speeds through uneven, natural terrain with various compositions and physical parameters. This paper presents a framework for high speed autonomous navigation based on the integrated real time traversability. Specifically, the proposed system performs real-time dynamic simulation and calculate maximum traversing velocity guaranteeing safe motion over rough terrain. The architecture of autonomous navigation is firstly presented for high-speed autonomous navigation. Then, the integrated real time traversability, which is composed of initial velocity profiling step, dynamic analysis step, road classification step and stable velocity profiling step, is introduced. Experimental results are presented that demonstrate the method for a $6{\times}6$ autonomous vehicle moving on flat terrain with bump.

• International Journal of Automotive Technology
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• 제3권2호
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• pp.71-78
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• 2002

A new control method far vision-based autonomous vehicle is proposed to determine navigation direction by analyzing lane information from a camera and to navigate a vehicle. In this paper, characteristic featured data points are extracted from lane images using a lane recognition algorithm. Then the vehicle is controlled using new Levenberg-Marquardt neural network algorithm. To verify the usefulness of the algorithm, another algorithm, which utilizes the geometric relation of a camera and vehicle, is introduced. The second one involves transformation from an image coordinate to a vehicle coordinate, then steering is determined from Ackermann angle. The steering scheme using Ackermann angle is heavily depends on the correct geometric data of a vehicle and a camera. Meanwhile, the proposed neural network algorithm does not need geometric relations and it depends on the driving style of human driver. The proposed method is superior than other referenced neural network algorithms such as conjugate gradient method or gradient decent one in autonomous lateral control .

• 자동차안전학회지
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• 제11권2호
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• pp.17-22
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• 2019

This paper describes the tracking algorithm performance evaluation for autonomous vehicle using a safety envelope based path. As the level of autonomous vehicle technologies evolves along with the development of relevant supporting modules including sensors, more advanced methodologies for path generation and tracking are needed. A safety envelope zone, designated as the obstacle free regions between the roadway edges, would be introduced and refined for further application with more detailed specifications. In this paper, the performance of the path tracking algorithm based on the generated path would be evaluated under safety envelop environment. In this process, static obstacle map for safety envelope was created using Lidar based vehicle information such as current vehicle location, speed and yaw rate that were collected under various driving setups at Seoul National University roadways. A level of safety was evaluated through CarSim simulation based on paths generated with two different references: a safety envelope based path and a GPS data based one. A better performance was observed for tracking with the safety envelop based path than that with the GPS based one.

• 자동차안전학회지
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• 제15권4호
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• pp.39-47
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• 2023

Research on the safety of autonomous vehicle is being conducted in various countries, including the European Union, and computer simulation techniques so called 'Virtual Tool Chain' are mainly used. As part of the crash safety study of autonomous vehicle, 25 car to car collision scenarios were provided as a result of a real accident-based accident reproduction analysis study conducted by a domestic research institution, and a vehicle crash analysis was performed using the FE car to car model of the Honda Accord. In order to analyze the results of the car to car simulation and to construct a database, major crash parameters were selected as impact speed, angle, location, and overlap, and a method of defining them in an indexed form was presented. In order to compare the crash severity of each scenario, a value obtained by integrating the resultant acceleration measured by the ACU of the vehicle was applied. The equivalent collision test mode was derived by comparing the crash severity of the regulation test mode, 30 deg rigid barrier mode, in the same way.

• 한국지능시스템학회논문지
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• 제19권3호
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• pp.381-387
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• 2009

본 논문에서는 자율주행 장치의 효율적인 자율주행을 위한 특징 맵 기반 SLAM(simultaneous localization and mapping)과 수정된 유전자 알고리즘을 이용한 경로계획을 제안하였다. 현재 연구되고 있는 자율주행 장치들에 있어서 가장 큰 문제점 중 하나는 환경 적응성이다. 이는 새로운 환경에서 자신의 위치를 인식해야 하는 경우와 "kid napping" 문제와 연계되어 자율주행 장치가 새로운 위치 혹은 알려지지 않은 위치에서 자신의 위치를 인식해야하는 경우로 구분된다. 본 논문에서는 이러한 환경 적응성 문제를 해결하기 위해 초음파 센서를 이용한 특징맵 기반 SLAM을 적용하였으며, 지능형 자율주행 장치의 효율적인 주행을 위해 수정된 유전자 알고리즘(genetic algorithm: GA)을 적용한다. 본 논문에서는 성능을 분석하기 위해 직접 설계 제작한 자율주행 장치를 대상으로 임의의 위치에서 자율주행 장치 스스로 자신의 위치를 인식한 후, 주어진 작업을 수행하기 위해 유전자 알고리즘을 통하여 최적화 된 경로를 따라 주행하는 가를 실험하였다. 실험 결과, 빠르고 최적화된 경로계획과 효율적인 SLAM이 가능함을 확인 할 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권8호
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• pp.4123-4141
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• 2019

Autonomous driving technology is divided into 0~5 levels. Of these, Level 5 is a fully autonomous vehicle that does not require a person to drive at all. The automobile industry has been trying to develop Level 5 to satisfy safety, but commercialization has not yet been achieved. In order to commercialize autonomous unmanned vehicles, there are several problems to be solved for driving safety. To solve one of these, this paper proposes 'A Deep Learning Part-diagnosis Platform(DLPP) based on an In-vehicle On-board gateway for an Autonomous Vehicle' that diagnoses not only the parts of a vehicle and the sensors belonging to the parts, but also the influence upon other parts when a certain fault happens. The DLPP consists of an In-vehicle On-board gateway(IOG) and a Part Self-diagnosis Module(PSM). Though an existing vehicle gateway was used for the translation of messages happening in a vehicle, the IOG not only has the translation function of an existing gateway but also judges whether a fault happened in a sensor or parts by using a Loopback. The payloads which are used to judge a sensor as normal in the IOG is transferred to the PSM for self-diagnosis. The Part Self-diagnosis Module(PSM) diagnoses parts itself by using the payloads transferred from the IOG. Because the PSM is designed based on an LSTM algorithm, it diagnoses a vehicle's fault by considering the correlation between previous diagnosis result and current measured parts data.

• 한국자동차공학회논문집
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• 제13권4호
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• pp.19-25
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• 2005

This paper presents a lateral control system for the autonomous navigation vehicle that was developed and tested by Robotics Centre of Ecole des Mines do Paris in France. A robust lane detection algorithm was developed for detecting different types of lane marker in the images taken by a CCD camera mounted on the vehicle. $^{RT}Maps$ that is a software framework far developing vision and data fusion applications, especially in a car was used for implementing lane detection and lateral control. The lateral control has been tested on the urban road in Paris and the demonstration has been shown to the public during IEEE Intelligent Vehicle Symposium 2002. Over 100 people experienced the automatic lateral control. The demo vehicle could run at a speed of 130km1h in the straight road and 50km/h in high curvature road stably.

• 한국콘텐츠학회논문지
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• 제19권12호
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• pp.452-459
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• 2019

자율주행자동차를 상용화하기 위해서는 모든 측면에서 안전성 테스트를 수행해야 한다. 자율주행자동차 기술의 구현을 고려할 때, 시내상황과 같은 복잡한 환경에서 발생할 수 있는 시나리오를 분석 할 필요가 있다. 자율 주행 차량이 기존의 교통 환경에서 정상적인 작동이 가능한지 여부의 평가도 중요하다. 또한 자율주행 자동차가 일반 차량과의 상호 작용을 검토하고, 도로에서 발생할 수 있는 사고에 대처할 필요가 있다. 본 연구는 도로환경에서 자율주행자동차의 평가 요소들을 기존의 ADAS와 같은 평가 프로토콜을 참고하여 자율주행 차량의 평가 방안을 모색하였다. 연구 결과는 다양한 기술 구현수준과 함께 다른 시험환경에 대한 자율 차량평가 방법을 수립하는데 반영하고자 한다.