• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.2
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• pp.865-871
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• 2011

Presently, the location and direction information are certainly needed for the autonomous vehicle of the ship. Among them, the direction information is a essential elements to automatic steering system. And the gyro-compass, the magnetic-compass and the GPS compass are the sensor indicating the direction. The gyro-compasses are mainly used in the large-sized ship of the GMDSS(Global Maritime Distress & Safety System). The precision and the reliability of the gyro-compasses are excellent but big volume and high price are disadvantage. The magnetic-compass has relatively fine precision and inexpensive price. However, the disadvantage is in the influence by the magnetism object including the steel structure of a ship, and etc. In the case of the GPS compass, the true north is indicated according to the change of the location information but in case of the minimum number of satellites or stopping of a ship or exercise in the error range, the exact direction cannot be obtained. In this paper, the performance of the GPS compass was improved by using the least-square curve fitting method for the mutual trade off of the angle sensor. The algorithm which improves the precision of an azimuth by applying the weighted value according to the size of covariance error was proposed with GPS-compass and magnetic compass. The characteristic and the performance of the proposed algorithm were analyzed and verified through experimentation. The applicability of the proposed algorithm was shown through the experimental result.

• International Journal of Highway Engineering
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• v.13 no.1
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• pp.119-127
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• 2011

The main purpose of this paper is to apply Zone 30 system which is being experimented in advanced country for the solution of controlling the residential street's speed to our country with the consideration of the real condition of our street and traffic and to run this system as an example to analyze the effect and at the same time, analyze the problem and get appropriate preparation for this system to be widespread. The area to run this model project is Goyang City Ilsan-Gu.($0.65km^2$) which is close with the commercial area reflecting the opinion of experts and an on-site verification by the National Police Agency T/F and is having a heavy pedestrian traffic and the risk of pedestrian accident. Firstly we defined residential street and residential street area to review the system and devided the residential street type to establish a plan of operation. Afterwards, we thoroughly examined the model project area and analyzed the problem and solution. We finally completed establishing a facilities by conference with a local autonomous entity with the improvement of facility's sketch at the analysis of the model project area. The result of effects analysis which we devided after and before of establishment is that vehicle speed be reduced 5~15km/h, and traffic accidents has decreased by 24 percent.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.271-278
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• 2014

In spite of that stop line and crosswalk should be aware of the most basic objects in transportation system, its features extracted are very limited. In addition to image-based recognition technology, laser and RF, GPS/INS recognition technology, it is difficult to recognize. For this reason, the limited research in this area has been done. In this paper, the algorithm to recognize the stop line and crosswalk is designed and implemented using image-based recognition technology with the images input through a vision sensor. This algorithm consists of three functions.; One is to select the area, in advance, needed for feature extraction in order to speed up the data processing, 'Region of Interest', another is to process the images only that white color is detected more than a certain proportion in order to remove the unnecessary operation, 'Color Pattern Inspection', the other is 'Feature Extraction and Recognition', which is to extract the edge features and compare this to the previously-modeled one to identify the stop line and crosswalk. For this, especially by using case based feature comparison algorithm, it can identify either both stop line and crosswalk exist or just one exists. Also the proposed algorithm is to develop existing researches by comparing and analysing effect of in-vehicle camera installation and changes in recognition rate of distance estimation and various constraints such as backlight and shadow.

• Journal of Sensor Science and Technology
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• v.7 no.2
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• pp.124-130
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• 1998

High-speed 3D vision systems are essential for autonomous robot or vehicle control applications. In our study, a stereo vision process has been developed. It consists of three steps : extraction of edges in right and left images, matching corresponding edges and calculation of the 3D map. This process is implemented in a VME 150/40 Imaging Technology vision system. It is a modular system composed by a display, an acquisition, a four Mbytes image frame memory, and three computational cards. Programmable accelerator computational modules are running at 40 MHz and are based on TMS320C31 DSP with a $64{\times}32$ bit instruction cache and two $1024{\times}32$ bit internal RAMs. Each is equipped with 512 Kbytes static RAM, 4 Mbytes image memory, 1 Mbytes flash EEPROM and a serial port. Data transfers and communications between modules are provided by three 8 bit global video bus, and three local configurable pipeline 8 bit video bus. The VME bus is dedicated to system management. Tasks between DSPs are distributed as follows: two DSPs are used to edges detection, one for the right image and the other for the left one. The last processor computes the matching process and the 3D calculation. With $512{\times}512$ pixels images, this sensor generates dense 3D maps at a rate of about 1 Hz depending of the scene complexity. Results can surely be improved by using a special suited multiprocessors cards.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.4
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• pp.153-163
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• 2017

This research is based on the previous research that personally preferred safe distance, rotating angle and speed are differentiated. Thus, we use machine learning model for recognizing maneuvering modes trained per personal or per similar driving pattern groups, and we evaluate automatic driving according to maneuvering modes. By utilizing driving knowledge, we subdivided 8 kinds of longitudinal modes and 4 kinds of lateral modes, and by combining the longitudinal and lateral modes, we build 21 kinds of maneuvering modes. we train the labeled data set per time stamp through RNN, LSTM and Bi-LSTM models by the trips of drivers, which are supervised deep learning models, and evaluate the maneuvering modes of automatic driving for the test data set. The evaluation dataset is aggregated of living trips of 3,000 populations by VTTI in USA for 3 years and we use 1500 trips of 22 people and training, validation and test dataset ratio is 80%, 10% and 10%, respectively. For recognizing longitudinal 8 kinds of maneuvering modes, RNN achieves better accuracy compared to LSTM, Bi-LSTM. However, Bi-LSTM improves the accuracy in recognizing 21 kinds of longitudinal and lateral maneuvering modes in comparison with RNN and LSTM as 1.54% and 0.47%, respectively.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.119-137
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• 2020

Over the last two decades, the attitudes to cars have changed from buying a car to sharing a car, especially among young people. Shared transport services and autonomous vehicles together can resolve the accessibility issue of shared transport services. Furthermore, they will make it possible to develop a new model of apartments without car parking. Therefore, the study estimated the demand for car sharing by young people and the running efficiency of car-sharing dealing with their car-based trip demand. The study chose nine apartment complexes for study sites where a majority of the residents were young people. The questionnaire survey was conducted to collect data on the trip demands of young people. The results showed that there are significant differences in the car-sharing use patterns and demand between the apartment houses located in the Capital region and non-capital region. Young people living in apartments in the Capital region used car sharing once per day per person for approximately 80 minutes per trip and tended to hire that between 8 AM and 10 AM. On the other hand, the young people living in apartments in the non-capital region used car sharing twice per day per person for approximately 200 minutes per trip. They tended to hire that frequently in the afternoon and evening as well as in the morning. The results also showed that a single car-sharing vehicle could deal with 3~4 trips per day in the Capital region and around 2 trips per day in the non-capital region.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.2
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• pp.821-828
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• 2011

This paper developed the algorithm for improving the performance the auto pilot in the autonomous vehicle system consisting of the Track keeping control, the Automatic steering, and the Automatic mooring control. The automatic steering is the control device that could save the voyage distance and cost of fuel by reducing the unnecessary burden of driving due to the continuous artificial navigation, and avoiding the route deviation. During the step of the ship autonomic navigation control, since the wind power or the tidal force could make the ship deviate from the fixed course, the automatic steering calculates the difference between actual sailing line and the set course to keep the ship sailing in the vicinity of intended course. first, we could get the transfer function for the modeling of ship according to the Nomoto model. Considering the maneuverability, we propose it as linear model with only 4 degree of freedoms to present the heading angle response to the input of rudder angle. In this paper, the model of ship is derived from the simplified Nomoto model. Since the proposed model considers the maximum angle and rudder rate of the ship auto pilot and also designs the Fuzzy controller based on existing PID controller, the performance of the steering machine is well improved.

• 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.

• The Journal of Society for e-Business Studies
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• v.23 no.3
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• pp.13-32
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• 2018

Btcoin has made a big progress through anonymity, decentralized authority, sharing economy, multi-ledger book-keeping, block-technology and the convenient financial vehicle. Bitcoin has the characteristics of mining and supply by decentralized suppliers, limited supply quantity and the partial money-like function as well as gold. The paper studies the hedge and safe-haven of Bitcoin and gold on daily frequency data over the period of July 20, 2010-Dec. 27, 2017 employing Asymmetric Vector GARCH. It finds that gold has the hedge and safe-haven against inflation and capital markets while Bitcoin has the weak hedge and the weak safe-haven. It shows insignificant effects of inflations of US and Korea on the volatilities of Bitcoin and gold. It also suggests the necessity of clearing of vagueness behind the anonymity for fair and transparent trade through the law application in the absence or fault in law (Lucken im Recht). following the spirit of the living constitution (lebendige gutes Recht oder Vorschrift). The relevant institutions are hoped to be given some of obligations such as registration, minimum required capital. report, disclosure, explanation, compliance and governance with autonomous corresponding rights. The study also suggests the reestablishment of the relevant financial law and taxation law. The hedge would not be successfully accomplished without the vigilant cautions of investors.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.167-175
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• 2019

Water color analysis allows non-destructive estimation of abundance of optically active water constituents in the water body. Recently, there have been increasing needs for light-weighted multispectral cameras that can be integrated with low altitude unmanned platforms such as drones, autonomous vehicles, and heli-kites, for the water color analysis by spectroradiometers. This study performs the preprocessing of the Micasense Rededge-M camera which recently receives a growing attention from the earth observation community for its handiness and applicability for local environment monitoring, and investigates the applicability of Rededge-M data for water color analysis. The Vignette correction and the band alignment were conducted for the radiometric image data from Rededge-M, and the sky, water, and solar radiation essential for the water color analysis, and the resultant remote sensing reflectance were validated with an independent hyperspectral instrument, TriOS RAMSES. The experiment shows that Rededge-M generally satisfies the basic performance criteria for water color analysis, although noticeable differences are observed in the blue (475 nm) and the near-infrared (840 nm) band compared with RAMSES.