• 자동차안전학회지
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• 제12권1호
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• pp.15-25
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• 2020

This paper presents a longitudinal control algorithm for ensuring takeover time of autonomous vehicle using V2V communication. In the autonomous driving of more than level 3, autonomous systems should control the vehicles by itself partially. However if the driver's intervention is required for functional safety, the driver should take over the control reasonably. Autonomous driving system has to be designed so that drivers can take over the control from autonomous vehicle reasonably for driving safety. In this study, control algorithm considering takeover time has been developed based on computation method of takeover time. Takeover time is analysed by conditions of longitudinal velocity of preceding vehicle in time-velocity plane. In addition, desired clearance is derived based on takeover time. The performance evaluation of the proposed algorithm in this study was conducted using 3D vehicle model with actual driving data in Matlab/Simulink environment. The results of the performance evaluation show that the longitudinal control algorithm can control while securing takeover time reasonably.

• ETRI Journal
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• 제36권6호
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• pp.968-978
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• 2014

Autonomous vehicle technology based on information technology and software will lead the automotive industry in the near future. Vehicle localization technology is a core expertise geared toward developing autonomous vehicles and will provide location information for control and decision. This paper proposes an effective vision-based localization technology to be applied to autonomous vehicles. In particular, the proposed technology makes use of numerical maps that are widely used in the field of geographic information systems and that have already been built in advance. Optimum vehicle ego-motion estimation and road marking feature extraction techniques are adopted and then combined by an extended Kalman filter and particle filter to make up the localization technology. The implementation results of this paper show remarkable results; namely, an 18 ms mean processing time and 10 cm location error. In addition, autonomous driving and parking are successfully completed with an unmanned vehicle within a $300m{\times}500m$ space.

• 한국HCI학회논문지
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• 제13권2호
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• pp.5-20
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• 2018

최근 상용화를 앞두고 국제적으로 활발히 연구되고 있는 완전 자율주행 자동차는 기존의 자동차에 비해 이동하는 시간을 활용할 수 있는 가능성이 높아져 생활공간이 될 것이라고 기대되면서 완전 자율주행 자동차의 실내공간 환경 조성에 대한 중요성이 매우 높아졌다. 완전 자율주행 자동차에서의 경험이 이동하는 목적을 넘어 생활공간에서 느끼는 만족으로 충족되기 위해서는 실내행위를 지원하는 공간이 조성되어야 한다, 이를 위해 실내공간에서의 사용자의 행위 유형이 무엇인지에 대한 고찰이 필요하다. 이러한 중요성에도 불구하고, 현재까지 완전 자율주행 자동차를 대상으로 실내행위에 대한 정의를 규정한 논문은 국내에서 매우 부족한 실정이다. 이에 본 연구는 자동차 내 실내행위 범위를 분석 및 유형화하고, 실내공간 배치에 따른 실내행위와 자동차의 특수성을 종합적으로 고려하여 완전 자율주행 자동차의 실내공간 설계 가이드라인을 제시하는 것을 목적으로 한다. 본 연구의 목적 달성을 위해 국내외 자동차 제조사 및 부품사의 자율주행 자동차 콘셉트 사례분석, 인간의 생활행위 및 생활시간, 공간과 행위에 대한 소비자 니즈(needs) 관련 문헌을 분석하여 완전 자율주행 자동차 내에서 요구되는 행위를 분석하였다. 그 결과 실내행위는 '업무 및 학습', '가정생활 및 개인관리', '휴식', '취미 및 사교'로 유형화되었고, 도출된 결과를 기반으로 종합적인 실내공간 설계 가이드라인을 제시하였다. 본 연구는 실내공간 설계를 위한 새로운 방향성을 고안하여 제시하였다는 점에 의의가 있으며, 본 연구를 바탕으로 하여 일상의 생활시간을 보낼 수 있는 공간인 새로운 미래의 완전 자율주행 자동차의 실내공간 환경 조성이 이루어질 수 있을 것으로 기대한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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• pp.274-281
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• 1998

This paper describes a visual control of autonomous vehicle using neural network. Visual control for road-following of autonomous vehicle is based on road image from camera. Road points on image are inputs of controller and vehicle speed and steering angle are outputs of controller using neural network. Simulation study confirmed the visual control of road-following using neural network. For experimental test, autonomous electric vehicle is designed and driving test is realized

• 한국지능시스템학회논문지
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• 제15권1호
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• pp.75-80
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• 2005

이 논문에서는 지능형 교통시스템에서 자율주행용 자계도로 3차원적으로 해석하여 차량의 위치를 검출하기 위한 시스템에 관하여 다룬다. 특히 자율주행시스템을 구성하는 핵심요소 중 하나인 위치검출시스템의 새로운 방법을 제안한다. 기존의 위치검출시스템은 자계와 차량의 위치 관계를 맵핑하는 원리를 이용한다. 이는 데이터의 저장이 필수적이며 대용량의 메모리를 요구되어 상용화시 고비용의 문제점을 가지고 있다. 이 논문에서는 기존 위치검출시스템이 가지는 문제점을 극복하기 위한 방법으로 신경망을 이용한 위치검출시스템을 제안한다. 그리고 제안한 위치검출시스템을 적용한 자율주행시스템을 설계한다. 설계한 자율주행시스템의 적용 가능성을 파악하기 위하여 자율주행실험을 행하고 이를 분석한다.

• 자동차안전학회지
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• 제14권1호
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• pp.20-25
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• 2022

This paper presents a development of simulation environment for validation of autonomous driving (AD) algorithm based on Robot Operating System (ROS). ROS is one of the commonly-used frameworks utilized to control autonomous vehicles. For the evaluation of AD algorithm, a 3D autonomous driving simulator has been developed based on LGSVL. Two additional sensors are implemented in the simulation vehicle. First, Lidar sensor is mounted on the ego vehicle for real-time driving environment perception. Second, GPS sensor is equipped to estimate ego vehicle's position. With the vehicle sensor configuration in the simulation, the AD algorithm can predict the local environment and determine control commands with motion planning. The simulation environment has been evaluated with lane changing and keeping scenarios. The simulation results show that the proposed 3D simulator can successfully imitate the operation of a real-world vehicle.

• 자동차안전학회지
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• 제14권1호
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• pp.39-44
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• 2022

This paper presents a development of throttle and brake controller for autonomous vehicle simulation environment. Most of 3D simulator control autonomous vehicle by throttle and brake command. Therefore additional longitudinal controller is required to calculate pedal input from desired acceleration. The controller consists of two parts, feedback controller and feedforward controller. The feedback controller is designed to compensate error between the actual acceleration and desired acceleration calculated from autonomous driving algorithm. The feedforward controller is designed for fast response and the output is determined by the actual vehicle speed and desired acceleration. To verify the performance of the controller, simulations were conducted for various scenarios, and it was confirmed that the controller can successfully follow the target acceleration.

• 자동차안전학회지
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• 제14권2호
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• pp.14-19
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• 2022

This paper presents motion planning algorithm that yields to intervening side lane vehicles in a congested traffic flow based on vehicle to vehicle (V2V) communication. Autonomous driving in dense traffic situation requires advanced driving performance in terms of vehicle interaction and risk mitigation. One of the most important functions necessary for congested traffic autonomous driving is to predict the lane change intention of the side lane target vehicle. However, implementing this function by using only environmental sensors has limitations. In this study, V2V communication is used to overcome the limitations and determine the intention of cut-in vehicles. Lane change intention of the intervening side lane vehicle is inferred by its longitudinal speed, steering angle, and turn signal light information received by the on-board-unit (OBU). Once the yield decision is made, the subject vehicle decelerates to generate sufficient clearance for the target vehicle to enter. Validation of the algorithm was conducted with actual autonomous test vehicles.

• 한국자동차공학회논문집
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• 제24권5호
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• pp.495-503
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• 2016

Regulation for the testing and operation of autonomous vehicles on public roadways has been recently developed all over the world. For example, the licensing standards and the evaluation technology for autonomous vehicles have been proposed in California, Nevada and EU. But specific safety evaluation scenarios for autonomous vehicles have not been proposed yet. This paper presents safety evaluation scenarios for extraordinary service permission of autonomous vehicles on highways. A total of five scenarios are selected in consideration of safety priority and real traffic situation. These scenarios are developed based on existing ADAS evaluation and simulation of autonomous vehicle algorithm. Also, Safety evaluation factors are developed based on ISO requirements, other papers and the current traffic regulations. These scenarios are investigated via computer simulation.

• 자동차안전학회지
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• 제13권4호
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• pp.106-113
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• 2021

In this study, The behavior of an autonomous vehicle in an intersection accident situation is predicted. Based on a representative intersection accident situation from actual intersection accident database, simulation was performed by applying the automatic emergency braking algorithm used in the autonomous driving system. Accident reconstruction was performed based on the accident report of the representative accident situation. After applying the autonomous driving system to the accident-related vehicle, the tendency of intersection accidents that may occur in autonomous vehicles was identified and analyzed.