• Information and Communications Magazine
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• v.33 no.4
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• pp.34-40
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• 2016

본고에서는 스마트 카/자율주행에서 스마트 폰과의 연결성에 대해 알아본다. 2008년 스마트폰이 막 등장한 시점에 자동차와의 연결에 대해 연구를 진행했던 당시에는 매우 획기적인 일이었지만 이제는 스마트폰과 스마트카는 연동되는 것이 당연한 시대가 되었다. 스마트폰의 기능 중 자동차와 연동하는 기능이 제공되기 시작했고 스마트 카의 핵심기능으로 마케팅 아이템으로도 사용되고 있다. 차량과 스마트 폰 연결성의 표준화 동향과 스마트 폰 플랫폼 사와 자동차 제조사간의 영역 다툼에 대해서도 살펴보고, 다가올 무인주행시대에서 스마트 폰의 역할에 대해 전망해 본다. 마지막으로 스마트 카와 자율주행 차의 컨테츠로 자율주행 네비게이션과 멀티미디어 컨텐츠에 대해서도 알아본다.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.733-734
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• 2016

무인 차량의 자율 주행을 위해 장애물 회피, 주행 가능 도로 판단 등의 기술이 연구되고 있다. 이러한 연구를 실제 환경에서의 자율 주행에 활용하기 위해서는 주변 환경에 동적으로 움직이는 장애물의 위치를 고려할 필요가 있다. 본 연구는 차량에 탑재된 LIDAR로부터 획득한 포인트의 분포 변화를 이용하여 차량 주변에 동적 장애물이 존재하는 지역을 검출하는 방법을 제안한다. 해당 방법은 포인트에 대한 통계치를 활용하여 동적 객체가 존재하는 영역을 추정함으로써 동적 객체 영역을 고속으로 탐색할 수 있다.

• 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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.7
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• pp.599-605
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• 2017

For an unmanned vehicle to be driven on the off-road terrain, it is necessary to consider the vehicle's stability. This paper suggests a path tracking controller for simulation of real-time vehicle stability analysis. The path tracking controller uses the preview distance to track the given trajectory. The disturbance moment is estimated using the yaw moment observer, and this information is used for compensation in the yaw moment control. On a curved path, the vehicle's desired velocity is determined from the curvature of the path. Because the vehicle is equipped with six independent motor driven wheels, the driving torques are distributed on all the wheels. The effectiveness of the path tracking controller is verified using ADAMS/MATLAB co-simulation.

• Journal of Advanced Navigation Technology
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• v.11 no.4
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• pp.378-387
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• 2007

In this paper, Authors develop and verify the algorithm for enhancing the performance of Unmanned vehicle's Autonomous navigation, and also propose the method of establishing much more precise Navigation locus. Unmanned vehicle has a destination, however orientation is not notified, which make it find the future orientation itself. Extended Kalman Filter make it access to the desirable direction, which coupled with INS and GPS is proposed in this paper. Sliding mode control could overcome the side slip and lateral minor movement of the vehicle. The test result would shows the effectiveness of Extended kalman filter and Slide mode control for the navigation.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2016.05a
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• pp.207-208
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• 2016

By according to increase the research for autonomous vehicle in automobile industries, the efficient of autonomous cargo truck instead of conventional driver cargo truck will be analyzed in logistics areas. The inland transportation has been serious problem such as traffic accident, traffic congestion in downtown, air pollution, noise and etc. even if it have a great effect to logistics industries. Especially there have to be improved its transportation schedule and sleepy driving in night which induced heavy accident in highway. In this study, the effectiveness of autonomous cargo truck will be analysed by considering the logistics cost including social effects.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.9
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• pp.369-374
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• 2014

Path generation methods are required for safe and efficient driving in unmanned autonomous vehicles. There are two kinds of paths: global and local. A global path consists of all the way points including the source and the destination. A local path is the trajectory that a vehicle needs to follow from a way point to the next in the global path. In this paper, we propose a novel method for local path generation through machine learning, with an effective curve function used for initializing the trajectory. First, reinforcement learning is applied to a set of candidate paths to produce the best trajectory with maximal reward. Then the optimal steering angle with respect to the trajectory is determined by training an artificial neural network. Our method outperformed existing approaches and successfully found quality paths in various experimental settings, including the cases with obstacles.

• Proceedings of the KAIS Fall Conference
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• 2012.05b
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• pp.520-523
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• 2012

본 연구에서 무인차량의 자율주행시스템은 차량제어시스템, 항법시스템, 장애물인식시스템, 통합시스템의 4개 서브시스템으로 구성 되어있고, GPS와 레이저스캐너를 이용한 무인차량의 장애물인식시스템을 개발하고자 한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.219-222
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• 2008

Recently, parking problems for an autonomous vehicle have attracted a great deal of attention and have been examined in many papers in the literature. In this paper we design a fuzzy logic based parking system at the slant parking space which is a important part for designing a autonomous parking system. We first design an optimal parking path for the slant space and present the simulation results of the fuzzy logic based parking system.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.11
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• pp.1047-1053
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• 2017

The range data acquired by 2D/3D LIDAR, a core sensor for autonomous navigation of an unmanned ground vehicle, is effectively used for ground modeling and obstacle detection. Within the ambiguous boundary of a road environment, however, LIDAR does not provide enough information to analyze the traversable region. This paper presents a new method to analyze a candidate area using the characteristics of LIDAR reflectivity for better detection of a traversable region. We detected a candidate traversable area through the front zone of the vehicle using the learning process of LIDAR reflectivity, after calibration of the reflectivity of each channel. We validated the proposed method of a candidate traversable region detection by performing experiments in the real operating environment of the unmanned ground vehicle.