• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.4
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• pp.59-73
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• 2016

The demand for subsurface transport is increasing. The users and the operators of road tunnels are exposed to risks with different causes. One main cause, however, is the traffic situation in the event of accidents. The importance of a Quantified Risk Assessment is increasing to quantify the safety of road tunnels and to balance the requirements (capacity, reliability, availability, maintainability and safety) of various stakeholders. Although there are classical methods for risk assessments, such as ETA and FTA. These methods are used for relatively simple cases because it could not relevantly reflect the diversity and relationship of the parameters. Therefore, a quantitative risk assessment based on Bayesian Probabilistic Networks considering interdependence between the parameters of a complex underground system as a double deck tunnel is provided.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.2
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• pp.281-288
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• 2009

In the paper, we try to develop the methodology for updating road networks of large-scale digital maps by using construction drawing data. For the purpose, it is pre-requite step to merge road networks detached in CAD drawing data. As such, tie points are identified in neighboring drawings and used for solving the parameters of 2D conformal transformation between drawings. Then, the merged road network in CAD data is transformed to the coordinate system of digital maps. In the process, IPs in the drawings are considered as control information and 2D affine transformation is selected for coordinate transformation. Through the experiments with real dataset, we can identify that the developed method is valid and generally applicable.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.265-275
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• 2023

The purpose of this paper is to devise an optimal route allocation algorithm for automated vehicle(AV) in monitoring quality of road infrastructure to support the road safety. The tasks of an AV in this paper include visiting node-links at least once during its operation and checking status of road infrastructure, and coming back to its depot.. In selecting optimal route, its priority goal is visiting the node-links with higher risks while reducing costs caused by operation. To deal with the problem, authors devised reward maximizing algorithm for AVs. To check its validity, the authors developed simple toy network that mimic node-link networks and assigned costs and rewards for each node-link. With the toy network, the reward maximizing algorithm worked well as it visited the node-link with higher risks earlier then chinese postman route algorithm (Eiselt, Gendreau, Laporte, 1995). For further research, the reward maximizing algorithm should be tested its validity in a more complex network that mimic the real-life.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.113-122
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• 2024

In this paper, we conducted a thorough investigation of existing simulators for running simulations of Vehicular Adhoc Networks (VANET) in realistic road environments, such as digital twins. After careful consideration, we chose a simulator that combines OSM (OpenStreetMap), SUMO (Simulation of Urban MObility), and OMNeT++ due to its open-source nature and efficient performance. Using this integrated simulator, we carried out VANET simulations in both simple virtual road environments and realistic road environments. Our findings revealed significant differences in VANET performance between the two types of environments, emphasizing the need to consider realistic road and traffic environments for reliable VANET operation. Furthermore, our simulations demonstrated significant performance variability, with performance degradation observed as vehicle density decreased and dynamic changes in network topology increased. These results underscore the importance of digital twin-based approaches in VANET research, highlighting the need to simulate real-world road and traffic conditions rather than relying on simple virtual road environments.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.55-64
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• 2018

In this paper, we propose fully convolutional neural networks based automatic detection of a pothole that directly causes driver's safety accidents and the vehicle damage. First, the training DB is collected through the camera installed in the vehicle while driving on the road, and the model is trained in the form of a semantic segmentation using the fully convolutional neural networks. In order to generate robust performance in a dark environment, we augmented the training DB according to brightness, and finally generated a total of 30,000 training images. In addition, a total of 450 evaluation DB was created to verify the performance of the proposed automatic pothole detection, and a total of four experts evaluated each image. As a result, the proposed pothole detection showed robust performance for missing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.8
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• pp.1933-1954
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• 2013

Multiple wireless devices are being widely deployed in Intelligent Transportation System (ITS) services on the road to establish end-to-end connection between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) networks. Vehicular ad hoc networks (VANETs) play an important role in supporting V2V and V2I communications (also called V2X communications) in a variety of urban environments with distinct topological characteristics. In fact, obstacles such as big buildings, moving vehicles, trees, advertisement boards, traffic lights, etc. may block the radio signals in V2X communications. Their impact has been neglected in VANET research. In this paper, we present a realistic and efficient radio propagation model to handle different sizes of static and moving obstacles for V2X communications. In the proposed model, buildings and large moving vehicles are modeled as static and moving obstacles, and taken into account their impact on the packet reception rate, Line-of-sight (LOS) obstruction, and received signal power. We use unsymmetrical city map which has many dead-end roads and open faces. Each dead-end road and open faces are joined to the nearest edge making a polygon to model realistic obstacles. The simulation results of proposed model demonstrates better performance compared to some existing models, that shows proposed model can reflect more realistic simulation environments.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.5
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• pp.115-124
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• 2008

In vehicular networks, vehicles should be able to authenticate each other to securely communicate with network-based infrastructure, and their locations and identifiers should not be exposed from the communication messages. however, when an accident occurs, the investigating authorities have to trace down its origin. As vehicles communicate not only with RSUs(Road Side Units) but also with other vehicles, it is important to minimize the number of communication flows among the vehicles while the communication satisfies the several security properties such as anonymity, authenticity, and traceability. In our paper, when the mutual authentication protocol is working between vehicles and RSUs, the protocol offers the traceability with privacy protection using pseudonym and MAC (Message Authentication Code) chain. And also by using MAC-chain as one-time pseudonyms, our protocol does not need a separate way to manage pseudonyms.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.153-163
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• 2023

This study is situated in the context of intelligent transport systems, where in-vehicle devices assist drivers to avoid accidents and therefore improve road safety. The vehicles present in a given area form an ad' hoc network of vehicles called vehicular ad' hoc network. In this type of network, the nodes are mobile vehicles and the messages exchanged are messages to warn about obstacles that may hinder the correct driving. Node mobilities make it impossible for inter-node communication to be end-to-end. Recognizing this characteristic has led to delay-tolerant vehicular networks. Embedded devices have small buffers (memory) to hold messages that a node needs to transmit when no other node is within its visibility range for transmission. The performance of a vehicular delay-tolerant network is closely tied to the successful management of the nodes' transit buffer. In this paper, we propose a message transit buffer management model for nodes in vehicular delay tolerant networks. This model consists in setting up, on the one hand, a policy of dropping messages from the buffer when the buffer is full and must receive a new message. This drop policy is based on the concept of intermediate node to destination, queues and priority class of service. It is also based on the properties of the message (size, weight, number of hops, number of replications, remaining time-to-live, etc.). On the other hand, the model defines the policy for selecting the message to be transmitted. The proposed model was evaluated with the ONE opportunistic network simulator based on a 4000m x 4000m area of downtown Bouaké in Côte d'Ivoire. The map data were imported using the Open Street Map tool. The results obtained show that our model improves the delivery ratio of security alert messages, reduces their delivery delay and network overload compared to the existing model. This improvement in communication within a network of vehicles can contribute to the improvement of road safety.

• International Journal of Automotive Technology
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• v.8 no.6
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• pp.753-760
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• 2007

In this paper, a roll and pitch estimation algorithm using a set of accelerometers and wireless sensor networks(S/N) is presented for use in a passenger vehicle. While an inertial measurement unit(IMU) is generally used for roll/pitch estimation, performance may be degraded in the presence of longitudinal acceleration and yaw motion. To compensate for this performance degradation, a new roll and pitch estimation algorithm is proposed that uses an accelerometer array, global positioning system(GPS) and in-vehicle networks to get information from yaw rate and roll rate sensors. Angular acceleration and roll and pitch approximation are first calculated based on vehicle kinematics. A discrete Kalman filter is then applied to estimate both roll and pitch more precisely by reducing noise from the running engine and from road disturbance. Finally, the feasibility of the proposed algorithm is shown by comparing its performance experimentally with that of an IMU in the framework of an indoor test platform as well as a test vehicle.

• Journal of Korean Society of Transportation
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• v.23 no.6 s.84
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• pp.91-102
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• 2005

When it comes to the process of information storage, people are likely to organize individual information into the forms of groups rather than independent attributes, and put them together in their brains. Likewise, in case of finding the shortest path, this study suggests that a Hierarchical Road Network(HRN) model should be selected to browse the most desirable route, since the HRN model takes the process mentioned above into account. Moreover, most of drivers make a decision to select a route from origin to destination by road hierarchy. It says that the drivers feel difference between the link travel tine which was measured by driving and the theoretical link travel time. There is a different solution which has predicted the link travel time to solve this problem. By using this solution, the link travel time is predicted based on link conditions from time to time. The predicated link travel time is used to search the shortest path. Stochastic Process model uses the historical patterns of travel time conditions on links. The HRN model has compared favorably with the conventional shortest path finding model in tern of calculated speeds. Even more, the result of the shortest path using the HRN model has more similar to the survey results which was conducted to the taxi drivers. Taxi drivers have a strong knowledge of road conditions on the road networks and they are more likely to select a shortest path according to the real common sense.