• Journal of Korean Tunnelling and Underground Space Association
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• v.14 no.2
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• pp.107-116
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• 2012

The experiment was performed to analyze the intersectional ventilation efficiency by intersection structure and Jet Fan in network-form road tunnel. For this, the size of real road tunnel was reduced by 1/45. To apply traffic inertia force when driving, blower fan was used to form an airflow in model tunnel and the intersectional efficiency was also investigated by measuring the speed at local point of the tunnel. To improve the reduction of ventilation caused by the structure character, Jet Fan was installed to optimize ventilation efficiency in tunnel.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.9 s.351
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• pp.142-148
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• 2006

Considering the domestic traffic environment and the increase of traffic accidents, we have been asked to exactly analyze the main causes of accidents for the accident-experienced drivers to be rehabilitated. In this thesis we present the development process and results of a driving simulator using the IPDE method in the interest of safe driving and driving rehabilitation. Through this Driving simulation development the rehabilitated driver has the possibility of experiencing the real driving situation with the driving aptitude and examines the reasons of accidents. Through the examinations the driver has the chance to correct the deformities of driving by choosing the explanatory scenes, and through this process the driver is able to develop the capability to react in the real situation. However this driving simulation system is one of the best developed, depending on weather and road condition the braking distance may change. Therefore the fuzzy rule and neural network have been used in this thesis to solve previously mentioned problem. The simulation exactly calculated the road and weather conditions to adjust the breaking intensity.

• Journal of Sensor Science and Technology
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• v.10 no.2
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• pp.134-141
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• 2001

This paper describes a method to steer an autonomous electric vehicle using magnetic fields. Magnets are embeded along the center of the road and a magneto-resistive sensor is mounted beneath the front bumper of the vehicle. As the vehicle moves along the road neural network controller controls the vehicle using measured magnetic field variation. Based on a single magnets modeling equation, we analyzed three dimensional magnetic field distributions of embeded magnets in series on the center of the road and performed a computer simulation using this results. In simulation study, straight and curved road was configured. The steering controller for the vehicle was designed using neural network and experiment was performed on the real embeded magnets using real autonomous electric vehicle. At the experiment we compensated the earth's magnetic fields and showed a good result driving an autonomous vehicle using proposed method.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.62-65
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• 2003

We propose an algorithm for almost unsupervised detection of linear structures, in particular, axes in road network and river, as seen in synthetics aperture radar (SAR) images. The first is local step and used to extract linear features from the speckle radar image, which are treated as road segment candidates. We present two local line detectors as well as a method for fusing information from these detectors. The second is global step, we identify the real roads among the segment candidates by defining a Markov random field (MRF) on a set of segments, which introduces contextual knowledge about the shape of road objects.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.121-124
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• 2000

We propose an algorithm for almost unsupervised detection of linear structures, in particular, axes in road network and river, as seen in synthetics aperture radar (SAR) images. The first is local step and used to extract linear features from the speckle radar image, which are treated as road segment candidates. We present two local line detectors as well as a method for fusing Information from these detectors. The second is hybrid step, we Identify the real roads among the segment candidates by defining a Markov random field (MRF) on a set of segments, which introduces contextual knowledge about the shape of road objects.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.221-224
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• 2000

We propose an algorithm for almost unsupervised detection of linear structures, in particular, axes in road network and river, as seen in synthetics aperture radar (SAR) images. The first is local step and used to extract linear features from the speckle radar image, which are treated as road segment candidates. We present two local line detectors as well as a method for fusing information from these detectors. The second is global step, we identify the real roads among the segment candidates by defining a Markov random field (MRF) on a set of segments, which introduces contextual knowledge about the shape of road objects.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.5
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• pp.385-391
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• 2013

As a solution for environmental problems caused by increasing number of vehicles, it is encouraged to use a bicycle as an environment-friendly transportation method. To vitalize the bicycle usage, it is a necessary to construct bicycle roads that are safe and suitable for users. Based on the previous research results, we assume the main purposes of bicycle usages are mainly local leisure activity and school commuting. Thus, the proposed method finds the shortest link between the existing bicycle road network and bicycle usage facilities such as leisure activity places or schools over public road network. Then, we carry out the RTK DGPS survey for the candidate links, and analyze the slopes of them. When the slope of a found link is larger than a threshold, an alternative link is re-found for the safety and convenience of a bicycle user. The proposed method is applied to the real bicycle road network in Mokpo, Chunnam and the results are discussed.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.337-346
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• 2006

In order to analyze the behavior of moving objects, a measure for determining the similarity of trajectories needs to be defined. Although research has been conducted that retrieved similar trajectories of moving objects in Euclidean space, very little research has been conducted on moving objects in the space defined by road networks. In terms of real applications, most moving objects are located in road network space rather than in Euclidean space. In similarity measure between trajectories, however, previous methods were based on Euclidean distance and only considered spatial similarity. In this paper, we define similarity measure based on POI and TOI in road network space. With this definition, we present methods to retrieve similar trajectories using spatio-temporal similarity between trajectories. We show clustering results for similar trajectories. Experimental results show that similar trajectories searched by each method and consistency rate between each method for the searched trajectories.

• ETRI Journal
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• v.44 no.2
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• pp.194-207
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• 2022

In the last decade, substantial progress has been achieved in intelligent traffic control technologies to overcome consistent difficulties of traffic congestion and its adverse effect on smart cities. Edge computing is one such advanced progress facilitating real-time data transmission among vehicles and roadside units to mitigate congestion. An edge computing-based deep reinforcement learning system is demonstrated in this study that appropriately designs a multiobjective reward function for optimizing different objectives. The system seeks to overcome the challenge of evaluating actions with a simple numerical reward. The selection of reward functions has a significant impact on agents' ability to acquire the ideal behavior for managing multiple traffic signals in a large-scale road network. To ascertain effective reward functions, the agent is trained withusing the proximal policy optimization method in several deep neural network models, including the state-of-the-art transformer network. The system is verified using both hypothetical scenarios and real-world traffic maps. The comprehensive simulation outcomes demonstrate the potency of the suggested reward functions.

• Tunnel and Underground Space
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• v.27 no.3
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• pp.132-145
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• 2017

Shock loss was not applied for the tunnel ventilation of road tunnel in the past. However, pressure losses due to the shock loss can be significant in network double-deck road tunnel in which combining and separating road structures exist. For the optimum ventilation design of network double-deck road tunnel, this study conducted 3D CFD numerical analysis for the shock loss at the combining and separating flows. The CFD model was made with the real-scale model that was the standard section of double-deck road tunnel. The shock loss coefficient of various combining and separating angles and road width was obtained and compared to the existing design values. As a result of the comparison, the shock loss coefficient of the $30^{\circ}$ separating flow model was higher and that of the two-lane combining flow model was lower. Since the combining and separating angles and road width can be important for the design of shock loss estimation, it is considered that this study can provide the accurate design factors for the calculation of ventilation system capacity. In addition, this study conducted 3D CFD analysis in order to calculate the shock loss coefficient of both combining and separating flows at flared intersection, and the result was compared with the design values of ASHRAE. The model that was not widened at the intersection showed three times higher at the most, and the other model that was widened at the intersection resulted two times higher shock loss coefficients.