• Electronics and Telecommunications Trends
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• v.34 no.1
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• pp.13-22
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• 2019

Various hyper-intelligent and ultra-realistic data-driven services are being increasingly developed with the goal of achieving a hyper-connected intelligent society. To sustain this trend, our research focuses on the integration and optimization of data-driven applications from several aspects such as delivery, storage, execution, and sharing of data and software, beyond the limitations of the existing network infrastructure. In this paper, we present important research issues of data-centric hyper-distributed autonomous infrastructure technologies.

• Electronics and Telecommunications Trends
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• v.35 no.5
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• pp.1-13
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• 2020

Recently, network infrastructure has evolved into a BizTech agile autonomous network to cope with the dynamic changes in the service environment. This survey presents the expectations from two different perspectives of the harmonization of network and artificial intelligence (AI) technologies. First, the paper focuses on the possibilities of AI technology for the autonomous network industry. Subsequently, it discusses how networks can play a role in the evolution of distributed AI technologies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5299-5320
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• 2019

Interest in self-driving vehicle research has been rapidly increasing, and related research has been continuously conducted. In such a fast-paced self-driving vehicle research area, the development of advanced technology for better convenience safety, and efficiency in road and transportation systems is expected. Here, we investigate research in self-driving vehicles and analyze the main technologies of driverless car software, including: technical aspects of autonomous vehicles, traffic infrastructure and its communications, research techniques with vision recognition, deep leaning algorithms, localization methods, existing problems, and future development directions. First, we introduce intelligent self-driving car and road infrastructure algorithms such as machine learning, image processing methods, and localizations. Second, we examine the intelligent technologies used in self-driving car projects, autonomous vehicles equipped with multiple sensors, and interactions with transport infrastructure. Finally, we highlight the future direction and challenges of self-driving vehicle transportation systems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.200-215
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• 2023

With the rapid advance of autonomous driving technology, the related vehicle market is experiencing explosive growth, and it is anticipated that the era of fully autonomous vehicles will arrive in the near future. However, along with the development of autonomous driving technology, questions regarding its safety and reliability continue to be raised. Concerns among technology adopters are increasing due to media reports of accidents involving autonomous vehicles. To promote the improvement of the safety of autonomous vehicles, it is essential to analyze previous accident cases and identify their causes. Therefore, in this study, we aimed to analyze the factors influencing the severity of autonomous vehicle accidents using previous accident cases and related data. The data used for this research primarily comprised autonomous vehicle accident reports collected and distributed by the California Department of Motor Vehicles (CA DMV). Spatial information on accident locations and additional traffic data were also collected and utilized. Given that the primary data used in this study were accident reports, a Poisson regression analysis was conducted to model the expected number of accidents. The research results indicated that the severity of autonomous vehicle accidents increases in areas with low lighting, the presence of bicycle or bus-exclusive lanes, and a history of pedestrian and bicycle accidents. These findings are expected to serve as foundational data for the development of algorithms to enhance the safety of autonomous vehicles and promote the installation of related transportation infrastructure.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.191-199
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• 2023

In this paper, we propose a method to extract the features of five sensor-only facilities built as infrastructure for autonomous cooperative driving, which are from point cloud data acquired by LiDAR. In the case of image acquisition sensors installed in autonomous vehicles, the acquisition data is inconsistent due to the climatic environment and camera characteristics, so LiDAR sensor was applied to replace them. In addition, high-intensity reflectors were designed and attached to each facility to make it easier to distinguish it from other existing facilities with LiDAR. From the five sensor-only facilities developed and the point cloud data acquired by the data acquisition system, feature points were extracted based on the average reflective intensity of the high-intensity reflective paper attached to the facility, clustered by the DBSCAN method, and changed to two-dimensional coordinates by a projection method. The features of the facility at each distance consist of three-dimensional point coordinates, two-dimensional projected coordinates, and reflection intensity, and will be used as training data for a model for facility recognition to be developed in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.14-28
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• 2018

Thanks to technical improvement on the vehicle to vehicle communication and the intelligent transportation system, gradual introduction of the autonomous vehicles is expected soon in the market. The study analyzes the autonomous vehicles' impacts on the network efficiencies. In order to measure the network efficiencies, the study applies the sequential procedures that combines the microscopic and macroscopic simulations. The microscopic simulation attends to the capacity changes due to the autonomous vehicles' proportions on the roadway while the macroscopic simulation utilizes the simulation results in order to identify the network-wide improvement. As expected, the autonomous vehicles efficiently utilizes the existing capacity of the roadway than the human driving does. Particularly, the maximum capacity improvements are expected by the 190.5% on the expressway. The significant capacity change is observed when the autonomous vehicles' proportions are about 80% or more. These improvements are translated into the macroscopic model, which also yields overall network efficiency improvement by the autonomous vehicles' penetration. However, the study identifies that the market debut of the autonomous vehicles does not promise the free flow condition, which implies the possible needs of the system optimal routing scheme for the era of the autonomous vehicles.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.161-172
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• 2023

Autonomous driving vehicles recognize the surroundings through various sensors mounted on the vehicle and control the vehicle based on the collected information. The level of autonomous driving technology is improving due to the development of sensor technology and algorithms that process collected data, but the implementation of perfect autonomous driving technology has not been achieved. To overcome these limitations, through autonomous cooperative driving centered on infrastructure. In this study, developed a position correction sign that provides a reference for positioning of autonomous vehicles. First of all, an analysis was performed on the current status of positioning technology for autonomous driving. And measure the number of point clouds for the 1st sample consisting of two square reflective surfaces and 2nd sample that increased the vertical length of each reflective surface. Experimental results show that both primary and secondary products are installed at least 15 m apart It could be recognized as a sensor, and it was confirmed that the secondary production that increased the length of the top and bottom had a higher number of point clouds than the primary production and better expressed the shape of the facility.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.3
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• pp.516-528
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• 2015

In this paper, we propose mobile ad hoc network configuration and its autonomous network construction method for efficiency and scalability of media streaming for mobile smart devices. To provide scalable network configuration for streaming traffic distribution, an IEEE 802.11 infrastructure network and ad hoc networks are hierarchically built. The proposed method autonomously configures a hierarchical streaming network by competition based on performance and states of devices and the wireless network, not depending on any specific nodes. Finally, we conduct performance measurement for the proposed configuration and analyze the experimental result.

• Computers and Concrete
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• v.33 no.4
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• pp.341-348
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• 2024

Ultra-high-performance concrete (UHPC) has received remarkable attentions in civil infrastructure due to its unique mechanical characteristics and durability. UHPC gains increasingly dominant in essential structural elements, while its unique properties pose challenges for traditional inspection methods, as damage may not always manifest visibly on the surface. As such, the need for robust inspection techniques for detecting cracks in UHPC members has become imperative as traditional methods often fall short in providing comprehensive and timely evaluations. In the era of artificial intelligence, computer vision has gained considerable interest as a powerful tool to enhance infrastructure condition assessment with image and video data collected from sensors, cameras, and unmanned aerial vehicles. This paper presents a computer vision-based approach employing deep learning to detect cracks in UHPC beams, with the aim of addressing the inherent limitations of traditional inspection methods. This work leverages computer vision to discern intricate patterns and anomalies. Particularly, a convolutional neural network architecture employing transfer learning is adopted to identify the presence of cracks in the beams. The proposed approach is evaluated with image data collected from full-scale experiments conducted on UHPC beams subjected to flexural and shear loadings. The results of this study indicate the applicability of computer vision and deep learning as intelligent methods to detect major and minor cracks and recognize various damage mechanisms in UHPC members with better efficiency compared to conventional monitoring methods. Findings from this work pave the way for the development of autonomous infrastructure health monitoring and condition assessment, ensuring early detection in response to evolving structural challenges. By leveraging computer vision, this paper contributes to usher in a new era of effectiveness in autonomous crack detection, enhancing the resilience and sustainability of UHPC civil infrastructure.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.258-272
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• 2022

The technology implementation method was diversified into an 'autonomous cooperative driving' method to overcome the limitations of a stand-alone autonomous vehicle with vehicle sensor-based autonomous driving. The autonomous cooperative driving method involves exchanging information between roadside infrastructure and autonomous vehicles. In this process, the concept of dynamic information (LDM), a target of cooperation, was established. But, evaluation methods and standards for dynamic information have not been established. Therefore, this study, a dynamic information evaluation method based on information on pedestrians within the moving objects. In addition, autonomous cooperative driving was demonstrated, and dynamic information was also verified through the evaluation method. The significance of this study is that it established the dynamic information evaluation methodology for autonomous cooperative driving for the first time. Based on this, this study is expected to contribute to the application of safe autonomous cooperative driving technology to the field.