• Journal of Korean Society of Transportation
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• v.25 no.3
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• pp.111-121
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• 2007

A transit assignment model can forecast the behaviors of transit users. thereby playing an important role In the evaluation of transit policies. Most existing transit assignment models are based on the models for passenger cars; therefore they cannot reflect the specific characteristics of transit modes. In addition most of the existing models are based on a single transit mode (bus or rail), and they cannot forecast the behaviors of transit users in a changing mass transportation system. The goal of this study is to overcome these problems with the exiting models and to develop a more realistic model. The newly developed model is based on mixed transit modes and is a stochastic model that can reflect the different preferences of each transit user for travel time and transfering. Data gathered from the Seoul metropolitan area's smart card are used to calibrate this model. This study is expected to be used for the evaluation of transportation policies and to attribute the development of transit revitalization strategies.

• Smart Media Journal
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• v.6 no.3
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• pp.15-20
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• 2017

Recently, the automotive industry has been studied about driver assistance systems for helping drivers to drive their cars easily by integrating them with the IT technology. This study suggests a method of detecting lanes, robust to road condition changes and applicable to lane departure warning and autonomous vehicles mode. The proposed method uses the method of detecting candidate areas by using the Gaussian filter and by determining the Otsu threshold value and edge. Moreover, the proposed method uses lane gradient and width information through the Hough transform to detect lanes. The method uses road lane information detected before to detect dashed lines as well as solid lines, calculates routes in which the lanes will be located in the next frame to draw virtual lanes. The proposed algorithm was identified to be able to detect lanes in both dashed- and solid-line situations, and implement real-time processing where applied to Raspberry Pi 2 which is open source hardware.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.3
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• pp.49-57
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• 2009

To minimze the degree of damage for the SMART highway's punctuality and safety after car-barrier collisions, the impact condition for longitudinal barriers of SMART highway was determined to be quite larger than the existing maximum impact condition. The impact condition consists of impact vehicles, impact velocities, and impact angles. To consider the occupant safety of passenger cars as much as possible, a small car with high risk during impact was selected as the impact vehicle for the evaluation of occupant risk. The impact velocity was determined to be 20% larger than the existing maximum impact velocity in order to include accident impact velocities as much as possible. The impact angle was determined to include most of expected accident impact angles. Computer simulations using various impact conditions were conducted for the existing domestic highest-performance medium and roadside barrier. How the suggested impact condition has an effect on the occupant safety was investigated. The existing domestic highest-performance medium and roadside barriers could not satisfy the suggested impact condition. New high-performance longitudinal barriers are required to minimize the degree of damage for the SMART highway's punctuality and safety after car-barrier collisions.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.4
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• pp.155-168
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• 2018

This study singled out 30 smart car features and surveyed 250 respondents. Assuming that the relationship between fulfillment of a feature or a customer need and the satisfaction with that feature is not necessarily linear, this study was conducted using Kano's method. Two devices, Timko Deviation(TD) and Kano Distribution Index(KDI), were devised to help evaluate resulting Kano table quantitatively. Previous research based on Kano's original framework showed the limit to the analysis of new or unfamiliar features: more than 85% of the features surveyed turned out to be either Attractive or Indifferent attributes. This study attempted a new empirical approach by applying customer experiences, price conditions, and customer self-stated importance. The results showed that customer experience of the surveyed features affected the overall satisfaction level, signifying that Kano's method should be conducted with care when analyzing emerging technologies such as smart cars. It is expected that this study would be utilized for better understanding of the perception and trends of customers regarding new technologies. This study also suggests a new approach to the analysis of customer requirements by providing price conditions.

• Smart Structures and Systems
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• v.19 no.2
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• pp.151-161
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• 2017

A new method is developed to estimate road profile in order to estimate IRI based on the ASTM standard. This method utilizes an accelerometer and a Dynamic Tire Pressure Sensor (DTPS) to estimate road roughness. The accelerometer measures the vertical axle acceleration. The DTPS, which is mounted on the tire's valve stem, measures dynamic pressure inside the tire while driving. Calibrated transfer functions are used to estimate road profile using the signals from the two sensors. A field test was conducted on roads with different quality conditions in the city of Brockton, MA. The IRI values estimated with this new method match the actual road conditions measured with Pavement Condition Index (PCI) based on the ASTM standard, images taken from an onboard camera and passengers' perceptions. IRI has negative correlation with PCI in general since they have overlapping features. Compared to the current method of IRI measurement, the advantage of this method is that a) the cost is reduced; b) more space is saved; c) more time is saved; and d) mounting the two sensors are universally compatible to most cars and vans. Therefore, this method has the potential to provide continuous and global monitoring the health of roadways.

• Journal of Advanced Navigation Technology
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• v.19 no.6
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• pp.587-594
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• 2015

Open operating system, Linux is the traditional Web, E-mail, DNS, FTP server, as well as being used in Cloud and Big data infrastructure. In addition, Linux is also used like a desktop or mobile devices, smart TV and cars. In particular, stepping up to the IoT era at this time is expected to be greater proportion occupied by Linux. As the use of Linux has increased security has emerged as an important factor. User management is core of Linux system security. In this paper, Classifying Linux user and analyzed the role of the user-specific file. Finally, we analyzed the linux management technologies and useful user security tools.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3488-3500
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• 2020

At the present time, the economy continues to flourish, and private cars have become the means of choice for most people. Therefore, the license plate recognition technology has become an indispensable part of intelligent transportation, with research and application value. In recent years, the convolution neural network for image classification is an application of deep learning on image processing. This paper proposes a strategy to improve the YOLO model by studying the deep learning convolutional neural network (CNN) and related target detection methods, and combines the OpenCV and TensorFlow frameworks to achieve efficient recognition of license plate characters. The experimental results show that target detection method based on YOLO is beneficial to shorten the training process and achieve a good level of accuracy.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.8
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• pp.633-642
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• 2016

It is important for advanced active safety systems and autonomous driving cars to get the accurate estimates of the nearby vehicles in order to increase their safety and performance. This paper proposes a sensor fusion method for radar and vision sensors to accurately estimate the state of the preceding vehicles. In particular, we performed a study on compensating for the lateral state error on automotive radar sensors by using a vision sensor. The proposed method is based on the Interactive Multiple Model(IMM) algorithm, which stochastically integrates the multiple Kalman Filters with the multiple models depending on lateral-compensation mode and radar-single sensor mode. In addition, a Probabilistic Data Association Filter(PDAF) is utilized as a data association method to improve the reliability of the estimates under a cluttered radar environment. A two-step correction method is used in the Kalman filter, which efficiently associates both the radar and vision measurements into single state estimates. Finally, the proposed method is validated through off-line simulations using measurements obtained from a field test in an actual road environment.

• Journal of Satellite, Information and Communications
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• v.7 no.2
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• pp.13-17
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• 2012

In recent years, type of the car is changing. Instead of cars that use internal combustion engines, we will use mainly eco-friendly electric vehicles. However, the utilization of electric vehicles brings enormous increase of power consumption. Thus, efficient power management and intelligent power consumption is required. Demand response can be effective measures of power consumption. In this paper, we present demand response technology applications, communication method, PLC application and simulation result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.383-403
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• 2021

With the development of mobile edge computing (MEC), some late-model application technologies, such as self-driving, augmented reality (AR) and traffic perception, emerge as the times require. Nevertheless, the high-latency and low-reliability of the traditional cloud computing solutions are difficult to meet the requirement of growing smart cars (SCs) with computing-intensive applications. Hence, this paper studies an efficient offloading decision and resource allocation scheme in collaborative vehicular edge computing networks with multiple SCs and multiple MEC servers to reduce latency. To solve this problem with effect, we propose a context-aware offloading strategy based on differential evolution algorithm (DE) by considering vehicle mobility, roadside units (RSUs) coverage, vehicle priority. On this basis, an autoregressive integrated moving average (ARIMA) model is employed to predict idle computing resources according to the base station traffic in different periods. Simulation results demonstrate that the practical performance of the context-aware vehicular task offloading (CAVTO) optimization scheme could reduce the system delay significantly.