• The Transactions of the Korea Information Processing Society
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• 제6권12호
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• pp.3710-3717
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• 1999

For the development of unmanned autonomous vehicle, it is essential to detect obstacles, especially vehicles, in the forward direction of navigation. In order to reliably exclude regions that do not contain obstacles and save a considerable amount of computational effort, it is often necessary to confine computation only to ROI(region of interest)s. A ROI is usually chosen as the interior region of the lane. We propose a computationally simple and efficient method for the detection of lanes based on Hough transform and quadratic curve fitting. The proposed method first employs Hough transform to get approximate locations of lanes, and then applies quadratic curve fitting to the locations computed by Hough transform. We have experimented the proposed method on real outdoor road scene. Experimental results show that our method gives accurate detection of straight and curve lanes, and is computationally very efficient.

• Journal of the Korean Society of Industry Convergence
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• 제20권2호
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• pp.177-186
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• 2017

With the latest developments in ICT(Information Communication Technology) technology, research on Intelligent Car, Connected Car that support autonomous driving or services is actively underway. It is true that the number of inputs linked to external connections is likely to be exposed to a malicious intrusion. I studied possible security issues that may occur within the Connected Car. A variety of security issues may arise in the use of CAN, the most typical internal network of vehicles. The data can be encrypted by encrypting the entire data within the CAN network system to resolve the security issues, but can be time-consuming and time-consuming, and can cause the authentication process to be carried out in the event of a certification procedure. To resolve this problem, CAN network system can be used to authenticate nodes in the network to perform a unique authentication of nodes using nodes in the network to authenticate nodes in the nodes and By encoding the ID, identifying the identity of the data, changing the identity of the ID and decryption algorithm, and identifying the cipher and certification techniques of the external invader, the encryption and authentication techniques could be detected by detecting and verifying the external intruder. Add a monitoring node to the CAN network to resolve this. Share a unique ID that can be authenticated using the server that performs the initial certification of nodes within the network and encrypt IDs to secure data. By detecting external invaders, designing encryption and authentication techniques was designed to detect external intrusion and certification techniques, enabling them to detect external intrusions.

• Ocean and Polar Research
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• 제43권4호
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• pp.321-334
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• 2021

'Marine mammals-based observations' refers to data acquisition activities from marine mammals by instrumenting CTD (Conductivity-Temperature-Depth) sensors on them for recording vertical profiles of ocean variables such as temperature and salinity during animal diving. It is a novel data collecting platform that significantly improves our abilities in observing extreme environments such as the Southern Ocean with low cost compared to the other conventional methods. Furthermore, the system continues to create valuable information until sensors are detached, expanding data coverage in both space and time. Owing to these practical advantages, the marine mammals-based observations become popular to investigate ocean circulation changes in the Southern Ocean. Although these merits may bring us more opportunities to understand ocean changes, the data should be carefully qualified before we interpret it incorporating shipboard/autonomous vehicles/moored CTD data. In particular, we need to pay more attention to salinity correction due to the usage of an unpumped-CTD sensor tagged on marine mammals. In this article, we introduce quality control methods for the marine mammals-based CTD profiles that have been developed in recent studies. In addition, we discuss strategies of quality control specifically for the seal-tagging CTD profiles, successfully having been obtained near Terra Nova Bay, Ross Sea, Antarctica since February 2021. It is the Korea Polar Research Institute's research initiative of animal-borne instruments monitoring in the region. We anticipate that this initiative would facilitate collaborative efforts among Polar physical oceanographers and even marine mammal behavior researchers to understand better rapid changes in marine environments in the warming world.

• Journal of Convergence for Information Technology
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• 제11권10호
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• pp.45-52
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• 2021

In this paper, we proposed an Atrous Residual U-Net (AR-UNet) to improve the segmentation accuracy of semantic segmentation method based on U-Net. The U-Net is mainly used in fields such as medical image analysis, autonomous vehicles, and remote sensing images. The conventional U-Net lacks extracted features due to the small number of convolution layers in the encoder part. The extracted features are essential for classifying object categories, and if they are insufficient, it causes a problem of lowering the segmentation accuracy. Therefore, to improve this problem, we proposed the AR-UNet using residual learning and ASPP in the encoder. Residual learning improves feature extraction ability and is effective in preventing feature loss and vanishing gradient problems caused by continuous convolutions. In addition, ASPP enables additional feature extraction without reducing the resolution of the feature map. Experiments verified the effectiveness of the AR-UNet with Cityscapes dataset. The experimental results showed that the AR-UNet showed improved segmentation results compared to the conventional U-Net. In this way, AR-UNet can contribute to the advancement of many applications where accuracy is important.

• Journal of the Korean Association of Geographic Information Studies
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• 제21권4호
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• pp.81-90
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• 2018

The 'Hyper-connected society' in which all objects such as people, device, place are connected via networks and share information being realized. As the information and communication environment changes, spatial information also faces a significant challenge. Korean government is striving to meet the social demand for spatial information that will bring 'Hyper-connectivity' such as autonomous vehicles, drones. Until now, however, it has only partially responded to urgent demand and has not prepared a fundamental countermeasure. In order to effectively and actively respond to the demand for spatial information that is needed in the Hyper-connected society, a strategy that can lead to mid- to long-term fundamental changes is needed. The purpose of this study is to analyze the future demand and application characteristics of spatial information confronted with a big paradigm shift called 'Hyper-connected society', and to search spatial information strategy that can cope with the demand of spatial information in future society.

• Journal of the Semiconductor & Display Technology
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• 제17권4호
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• pp.21-26
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• 2018

Recently, the emergence of 3D cameras, 3D scanners and various cameras including Lidar is expected to be applied to applications such as AR, VR, and autonomous mobile vehicles that deal with 3D data. In Particular, the 3D point cloud data consisting of tens to hundreds of thousands of 3D points is rapidly increased in capacity compared with 2D data, Efficient encoding / decoding technology for smooth service within a limited bandwidth, and efficient service provision technology for differentiating the area of interest and the surrounding area are needed. In this paper, we propose a new quality parameter considering characteristics of 3D point cloud instead of quality change based on assumed video codec in MPEG V-PCC used in 3D point cloud compression, 3D Grid division method and representation for selectively transmitting 3D point clouds according to user's area of interest, and propose a new 3D Manifesto. By using the proposed technique, it is possible to generate more bitrate images, and it is confirmed that the efficiency of network, decoder, and renderer can be increased while selectively transmitting as needed.

• Smart Media Journal
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• 제8권2호
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• pp.66-73
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• 2019

Recently, object recognition technology using computer vision has attracted attention as a technology to replace sensor-based object recognition technology. It is often difficult to commercialize sensor-based object recognition technology because such approach requires an expensive sensor. On the other hand, object recognition technology using computer vision may replace sensors with inexpensive cameras. Moreover, Real-time recognition is viable due to the growth of CNN, which is actively introduced into other fields such as IoT and autonomous vehicles. Because object recognition model applications demand expert knowledge on deep learning to select and learn the model, such method, however, is challenging for non-experts to use it. Therefore, in this paper, we analyze the structure of deep - learning - based object recognition models, and propose a platform that can automatically select a deep - running object recognition model based on a user 's desired condition. We also present the reason we need to select statistics-based object recognition model through conducted experiments on different models.

• Journal of the Korea Society for Simulation
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• 제28권1호
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• pp.93-98
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• 2019

In case of unmanned aerial vehicles used in modern society, there has been a problem where a human operator should be still needed to control the UAV because of a lower level of autonomy. In this paper, genetic algorithm is selected as a methodology for the autonomy accomplishment and then we verify a possibility of UAV autonomy by applying the GA. The landing is one of the important classical tasks on aerial vehicle and the lunar Landing is one of the most historical events. Autonomy possibility of computer-simulated UAV is verified by landing autonomy method of a falling body equipped with a propulsion system similar to the lunar Lander. When applying the GA, the genom is encoded only with 4 actions (left-turn, right-turn, thrust, and free-fall) and applied onto the falling body, Then we applied the major operations of GA and achieved a success experiment. A major contribution is to construct a simulated UAV where an autonomy of UAV can be accomplished while minimizing the sensor dependency. Also we implemented a test-bed where the possibility of autonomy accomplishment by applying the GA can be verified.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2019년도 춘계학술대회
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• pp.169-170
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• 2019

Lately, as artificial intelligence becomes a source of global competitiveness, the government is strategically fostering artificial intelligence that is the base technology of future new industries such as autonomous vehicles, drones, and robots. Domestic artificial intelligence research and services have been launched mainly in Naver and Kakao, but their size and level are weak compared to overseas. Recently, deep learning has been conducted in recent years while recording innovative performance in various pattern recognition fields including speech recognition and image recognition. In addition, deep running has attracted great interest from industry since its inception, and global information technology companies such as Google, Microsoft, and Samsung have successfully applied deep learning technology to commercial products and are continuing research and development. Therefore, we will look at artificial intelligence which is attracting attention based on previous research.

• Journal of Auto-vehicle Safety Association
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• 제13권3호
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• pp.78-85
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• 2021

Recently a traffic accident of heavy duty vehicles under the mandatory installation of ADAS (Advanced Driver Assistance System) is often reported in the media. Heavy duty vehicle accidents are normally occurring a high number of passenger's injury. According to report of Insurance Institute for Highway Safety, FCW (Forward Collision Warning) and AEB (Automatic Emergency Braking) were associated with a statistically significant 12% reduction in the rate of police-reportable crashes per vehicle miles traveled, and a significant 41% reduction in the rear-end crash rate of large trucks. Also many countries around the world, including Korea, are studying the effects of ADAS installation on accident reduction. Traffic accident statistics of passenger vehicle for business purpose in TMACS (Traffic safety information Management Complex System in Korea) tends to remarkably reduce the number of deaths due to the accident (2017(211), 2018(170), 2019(139)), but the number of traffic accidents (2017(8,939), 2018(9,181), 2019(10,095)) increases. In this paper, it is introduced a traffic accident case that could lead to high injury traffic accidents by being equipped with AEB in a bus. AEB reduces accidents and damage in general but malfunction of AEB could occur severe accident. Therefore, proper education is required to use AEB system, simply instead of focusing on developing and installing AEB to prevent traffic accidents. Traffic accident of AEB equipped vehicle may arise a new dispute between a driver's fault and vehicle defect. It is highly recommended to regulate an advanced event data recorder system.