• 한국ITS학회 논문지
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• 제22권6호
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• pp.264-283
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• 2023

자율주행차량 사고는 일반차량 사고와 다르게 기술적 문제, 환경, 운전자와의 상호작용 등 다양한 요인에 기인한 사고 발생 가능성이 존재한다. 향후 자율주행 기술의 진보로 기존의 사고원인 이외에도 새로운 이슈들이 대두될 것으로 예상되며, 이에 대응하기 위한 다양한 시나리오 기반의 접근법이 필요하다. 본 연구에서는 자율주행 사고 리포트인, CA DMV collision report와 자율주행모드 해제 보고서인 Disengagement report, 자율주행 실제 사고영상을 수집하여 자율주행차량 교통사고 시나리오를 개발하였다. 시나리오는 ISO 26262의 기능안전 시스템 failure mode에 기반하여 도출되었으며, 자율주행 기능의 다양한 이슈를 반영하고자 하였다. 본 연구를 통해 도출된 자율주행차량 시나리오는 향후 다양한 자율주행차량 교통사고 예방과 대비에 기여할 뿐만 아니라 자율주행 기술의 안전성을 향상시키는 데 중요한 역할을 할 것으로 기대한다.

• International Journal of Control, Automation, and Systems
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• 제6권2호
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• pp.282-287
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• 2008

In distributed mobile robot systems, autonomous robots accomplish complicated tasks through intelligent cooperation with each other. This paper presents behavior learning and online distributed evolution for cooperative behavior of a group of autonomous robots. Learning and evolution capabilities are essential for a group of autonomous robots to adapt to unstructured environments. Behavior learning finds an optimal state-action mapping of a robot for a given operating condition. In behavior learning, a Q-learning algorithm is modified to handle delayed rewards in the distributed robot systems. A group of robots implements cooperative behaviors through communication with other robots. Individual robots improve the state-action mapping through online evolution with the crossover operator based on the Q-values and their update frequencies. A cooperative material search problem demonstrated the effectiveness of the proposed behavior learning and online distributed evolution method for implementing cooperative behavior of a group of autonomous mobile robots.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.1349-1354
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• 1990

This paper deals with the control of autonomous vehicle in the production systems. Presently, there is a significant interest in autonomous vehicles which are capable of intelligent motion (and action) without requiring a guide track to follow. This paper describes a PI-F adaptive control algorithm, which is used to drive an experimental autonomous vehicle along a given trajectory. The simulation results characterizing the accuracy og the algorithm are presented.

• 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

• 제어로봇시스템학회논문지
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• 제17권5호
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• pp.451-459
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• 2011

Fundamentally, there are 5 systems are needed for autonomous navigation of unmanned ground vehicle: Localization, environment perception, path planning, motion planning and vehicle control. Path planning and motion planning are accomplished based on result of the environment perception process. Thus, high reliability of localization and the environment perception will be a criterion that makes a judgment overall autonomous navigation. In this paper, via map matching using vehicle dynamic model and LIDAR sensors, replace high price localization system to new one, and have researched an algorithm that lead to robust autonomous navigation. Finally, all results are verified via actual unmanned ground vehicle tests.

• ETRI Journal
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• 제43권4호
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• pp.580-593
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• 2021

When operating in confined spaces or near obstacles, collision-free path planning is an essential requirement for autonomous exploration in unknown environments. This study presents an autonomous exploration technique using a carefully designed collision-free local planner. Using LiDAR range measurements, a local end-point selection method is designed, and the path is generated from the current position to the selected end-point. The generated path showed the consistent collision-free path in real-time by adopting the Euclidean signed distance field-based grid-search method. The results consistently demonstrated the safety and reliability of the proposed path-planning method. Real-world experiments are conducted in three different mines, demonstrating successful autonomous exploration flights in environment with various structural conditions. The results showed the high capability of the proposed flight autonomy framework for lightweight aerial robot systems. In addition, our drone performed an autonomous mission in the tunnel circuit competition (Phase 1) of the DARPA Subterranean Challenge.

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.67-72
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• 2023

In the past decade, Autonomous Vehicle Systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as well as road safety and the future of transportation systems. The fusion of light detection and ranging (LiDAR) and camera data in real-time is known to be a crucial process in many applications, such as in autonomous driving, industrial automation and robotics. Especially in the case of autonomous vehicles, the efficient fusion of data from these two types of sensors is important to enabling the depth of objects as well as the classification of objects at short and long distances. This paper presents classification of objects using CNN based vision and Light Detection and Ranging (LIDAR) fusion in autonomous vehicles in the environment. This method is based on convolutional neural network (CNN) and image up sampling theory. By creating a point cloud of LIDAR data up sampling and converting into pixel-level depth information, depth information is connected with Red Green Blue data and fed into a deep CNN. The proposed method can obtain informative feature representation for object classification in autonomous vehicle environment using the integrated vision and LIDAR data. This method is adopted to guarantee both object classification accuracy and minimal loss. Experimental results show the effectiveness and efficiency of presented approach for objects classification.

• 한국지능시스템학회논문지
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• 제18권5호
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• pp.579-585
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• 2008

최근 외부의 개입 없이 스스로 주변 환경을 파악하고 목적지까지의 이동경로를 생성하여 자율 주행하는 지능형 시스템에 관한 연구가 활발히 진행되고 있다. 이러한 자율 주행 시스템은 기본적으로 운행 중에 사고가 발생하지 않고 안전하게 목표점까지 이동해야 한다. 이를 위해 다양한 센서를 자율 주행 시스템에 장착하여 장애물을 인식하는 방법을 사용하고 있다. 본 논문에서는 레이저 변위 센서 및 카메라를 장착한 온톨로지 기반의 자율주행 시스템을 설계하고, 감지정보를 개념화하는 방법을 제안한다. 그리고 자율 주행 시스템의 자율 주행 실험을 통해 제안 기법의 타당성을 보인다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.220-223
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• 2003

In this paper, we optimize distributed autonomous robotic system based on artificial immune system. Immune system has B-cell and T-cell that are two major types of lymphocytes. B-cells take part in humoral responses that secrete antibodies and T-cells take part in cellular responses that stimulate or suppress cells connected to the immune system. They have communicating network equation, which have many parameters. The distributed autonomous robotics system based on this artificial immune system is modeled on the B-cells and T-cells system. So performance of system is influenced by parameters of immune network equation. We can improve performance of Distributed autonomous robotics system based on artificial immune system.

• 문화기술의 융합
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• 제9권4호
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• pp.375-381
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• 2023

미래 전장 양상이 초연결성에 기반한 전장 네트워크 발전에 기반할수록 그 결과 또한 예측할 수 없는 불확실성이 더욱 커질 것으로 예견된다. 이런 환경에서 인공지능에 기반한 자율무기체계 도입은 그 불확실성을 더욱 증대시킨다. 불확실성이 증대된 환경에서 공동의 작업으로 인해 도출된 결과에 대한 책임을 평가하는 것은 그 중요성에도 불구하고 선명한 방향을 정하지 못하고 있으며 누구도 충분한 책임을 지지 않는 책임간극(responsibility gap)에 대한 우려도 있다. 이에 따라 이 글에서는 자율무기체계의 운용양상을 분석하고 그 과정에서 발생할 수 있는 윤리적 문제로서 책임간극의 문제를 로버트 스패로우의 이론을 통하여 고찰해볼 것이다. 그의 주장을 면밀히 살펴보고 치명적 자율무기체계(lethal autonomous weapon system, LAWS)의 운용에서 책임간극은 극복 가능한 것임을 밝힌다. 간극이 아닌 책임의 프레임이 앞으로 다가올 전장 환경에 더 적합함을 보여줌으로서 신중한 무기체계 개발 및 사용의 기반을 마련하고자 한다.