• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.30-39
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• 2007

This paper is concerned with development of system to detect an obstructive vehicle which is an essential prerequisite for autonomous driving system of ASV(Advanced Safety Vehicle). First, the boundary of driving lanes is detected by a Kalman filter through the front image obtained by a CCD camera. Then, lanes are recognized by regression analysis of the detected boundary. Second, parameters of road curvature within the detected lane are used as input in error-BP algorithm to recognize the driving direction. Finally, an obstructive vehicle that enters into the detection region can be detected through setting detection fields of the front and lateral side. The experimental results showed that the proposed system has high accuracy more than 90% in the recognition rate of driving direction and the detection rate of an obstructive vehicle.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.1
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• pp.99-107
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• 2014

Abruptly occurred obstacles on highway threaten driving safety. Radar draws the attention to the collision avoidance system because it can be fully operational in all weather, and day and night condition. This paper presents the design, implementation and performance test results of pulsed Doppler radar system for detection and warning of road obstacles. The system is designed to consider highway environment and detection capability about various fixed and moving obstacles. The system consists of 4 subsystems, which include antenna unit, transmitter and receiver unit, radar signal & data processing unit, and controller & display unit. The core technologies include clutter map based change detection for fixed obstacles detection, Doppler estimation for velocity detection of moving targets, and azimuth angle estimation method using monopulse for lane estimation and tracking. The design performance of the developed radar system is verified through experiments using a fixed reference target and moving vehicles in test highway.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.2
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• pp.95-111
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• 2021

As the population decreases in an aging society, the average age of drivers increases. Accordingly, the elderly at high risk of being in an accident need autonomous-driving vehicles. In order to secure driving safety on the road, several technologies to respond to various obstacles are required in those vehicles. Among them, technology is required to recognize static obstacles, such as poor road conditions, as well as dynamic obstacles, such as vehicles, bicycles, and people, that may be encountered while driving. In this study, we propose a deep neural network algorithm capable of simultaneously detecting these two types of obstacle. For this algorithm, we used 1,418 road images and produced annotation data that marks seven categories of dynamic obstacles and labels images to indicate road damage. As a result of training, dynamic obstacles were detected with an average accuracy of 46.22%, and road surface damage was detected with a mean intersection over union of 74.71%. In addition, the average elapsed time required to process a single image is 89ms, and this algorithm is suitable for personal mobility vehicles that are slower than ordinary vehicles. In the future, it is expected that driving safety with personal mobility vehicles will be improved by utilizing technology that detects road obstacles.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.853-856
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• 2008

센서 네트워크의 한 개의 노드인 모바일 센서 차량들은 주위를 이동하면서 장애물을 탐지하고, 자신이 만든 지도를 서로 교환하여 합쳐 자신의 위치를 지역화한다. 이를 위해서 모바일 센서 차량들은 각종 센서를 탑재하여 자신의 위치를 파악한다. 이 논문에서는 데드-레코닝, 카메라, 그리고 RSSI 를 사용한 모바일 센서 차량의 지역화를 개별적으로 실험해봄으로써 거리에 따른 정확성을 알아보고, 이로써 각각 방식들이 가진 장점을 융합하여 보다 낳은 지역화할 수 있는지 살펴본다.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.55-59
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• 2008

For effective mobility control of UGV(unmanned ground vehicle), the terrain cover classification is an important component as well as terrain geometry recognition and obstacle detection. The vision based terrain cover classification algorithm consists of pre-processing, feature extraction, classification and post-processing. In this paper, we present a method to classify terrain covers based on the color and texture information. The color space conversion is performed for the pre-processing, the wavelet transform is applied for feature extraction, and the SVM(support vector machine) is applied for the classifier. Experimental results show that the proposed algorithm has a promising classification performance.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.329-332
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• 2023

3D Object Detection using deep neural network has been developed a lot for obstacle detection in autonomous vehicles because it can recognize not only the class of target object but also the distance from the object. But in the case of 3D Object Detection models, the detection performance for distant objects is lower than that for nearby objects, which is a critical issue for autonomous vehicles. In this paper, we introduce a technique that increases the performance of 3D object detection models, particularly in recognizing distant objects, by generating virtual 3D vehicle data and adding it to the dataset used for model training. We used a spherical point tracing method that leverages the characteristics of 3D LiDAR sensor data to create virtual vehicles that closely resemble real ones, and we demonstrated the validity of the virtual data by using it to improve recognition performance for objects at all distances in model training.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.6
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• pp.1006-1012
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• 2010

For the autonomous navigation of an unmanned ground vehicle in the rough terrain and combat, the dust environment should necessarily be overcome. Therefore, we propose a robust obstacle detection methodology using laser range sensor and radar. Laser range sensor has a good angle and distance accuracy, however, it has a weakness in the dust environment. On the other hand, radar has not better the angle and distance accuracy than laser range sensor, it has a robustness in the dust environment. Using these characteristics of laser range sensor and radar, we use laser range sensor as a main sensor for normal times and radar as a assist sensor for the dust environment. For fusion of laser range sensor and radar information, the angle and distance data of the laser range sensor and radar are separately transformed to the angle and distance data of virtual range sensor which is located in the center of the vehicle. Through distance comparison of laser range sensor and radar in the same angle, the distance data of a fused virtual range sensor are changed to the distance data of the laser range sensor, if the distance of laser range sensor and radar are similar. In the other case, the distance data of the fused virtual range sensor are changed to the distance data of the radar. The suggested methodology is verified by real experiment.

• Korean Journal of Remote Sensing
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• v.31 no.3
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• pp.267-279
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• 2015

In the development of autonomous vehicles, the importance of LIDAR sensors becomes larger. For sensor selection or algorithm development, it is difficult to test expensive LIDAR sensors mounted on a vehicle under various driving environment. In this study, we developed a simulator that is generally applicable for various vehicle LIDAR sensors based on the generalized geometric modeling of the common processes associated with vehicle LIDAR sensors. By configuring this simulator with the specific sensors being widely used, we performed the data simulation and quality analysis. Also, we applied the simulation data to obstacle detection and evaluated the applicability of the selected sensor. The developed simulator enables various experiments and algorithm development in parallel with hardware implementation prior to the deployment and operation of a sensor.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.11
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• pp.1285-1294
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• 2010

This study describes the design and verification of short range UWB(Ultra Wideband) imaging radar that is able to display high resolution radar image for front area of a UGV(Unmanned Ground Vehicle). This radar can help a UGV to navigate autonomously as it detects and avoids obstacles through foliage. We describe the relationship between bandwidth of transmitting signal and range resolution. A vivaldi antenna is designed and it's radiation pattern and reflection are measured. It is easy to make array antenna because of small size and thin shape. Aperture size of receiving array antenna is determined by azimuth resolution of radar image. The relation of interval of receiving antenna array, image resolution and aliasing of target on a radar image is analyzed. A vector network analyzer is used to obtain the reflected signal and corner reflectors as targets are positioned at grass field. Applicability of the proposed radar to UGV is proved by analysis of image resolution and penetrating capability for grass in the experiment.