• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.187-194
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• 2012

Vehicular communication networking is one of the most important building blocks of Intelligent Transportation System(ITS). The vehicular communication network is a wireless communication system enabling vehicles to communicate with each other as well as with roadside base stations. Especially, Wi-Fi based vehicle-to-infrastructure(V2I) communication is an emerging solution to improve the safety, traffic efficiency, and comfort of passengers. In this paper, we proposed a new communication hub platform for vehicles, and explained vehicle communication technology in short. Through car simulation results, we show thar our proposed system reduces signaling interference.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.1
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• pp.79-85
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• 2011

Bridge Weigh-in-Motion(BWIM) system calculates a travelling vehicle's weight without interruption of traffic flow by analyzing the signals that are acquired from various sensors installed in the bridge. BWIM system or data accumulated from the BWIM system can be utilized to development of updated live load model for highway bridge design, fatigue load model for estimation of remaining life of bridges, etc. Field test with moving trucks including various load cases should be performed to guarantee successful development of precise BWIM system. In this paper, a numerical simulation technique is adopted as an alternative or supplement to the vehicle traveling test that is indispensible but expensive in time and budget. The constructed numerical model is validated by comparison experimentally measured signal with numerically generated signal. Also vehicles with various dynamic characteristics and travelling conditions are considered in numerical simulation to investigate the variation of bridge responses. Considered parameters in the numerical study are vehicle velocity, natural frequency of the vehicle, height of entry bump, and lateral position of the vehicle. By analyzing the results, it is revealed that the lateral position and natural frequency of the vehicle should be considered to increase precision of developing BWIM system. Since generation of vehicle travelling signal by the numerical simulation technique costs much less than field test, a large number of test parameters can effectively be considered to validate the developed BWIM algorithm. Also, when artificial neural network technique is applied, voluminous data set required for training and testing of the neural network can be prepared by numerical generation. Consequently, proposed numerical simulation technique may contribute to improve precision and performance of BWIM systems.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.760-767
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• 2018

Advanced driver assistance system(ADAS) is a major technique in the intelligent vehicle field. The techniques for ADAS can be separated in two classes, i.e., methods that directly control the movement of vehicle and that indirectly provide convenience to driver. In this paper, we propose a novel system that gives a visual assistance to driver by translating a night road image to a day road image. We use the black box images capturing the front road view of vehicle as inputs. The black box images are cropped into three parts and simultaneously translated into day images by the proposed image translation module. Then, the translated images are recollected to original size. The experimental result shows that the proposed method generates realistic images and outperforms the conventional algorithms.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.277-282
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• 2006

It is well known that Kalman filtering is an optimal real-time data fusion method for GPS/INS integration. However, it has some limitations in terms of stability, adaptability and observability. A Kalman filter can perform optimally only when its dynamic model is correctly defined and the noise statistics for the measurement and process are completely known. It is found that estimated Kalman filter states could be influenced by several factors, including vehicle dynamic variations, filter tuning results, and environment changes, etc., which are difficult to model. Neural networks can map input-output relationships without apriori knowledge about them; hence a proper designed neural network is capable of learning and extracting these complex relationships with enough training. This paper presents a GPS/INS integrated system that combines Kalman filtering and neural network algorithms to improve navigation solutions during GPS outages. An Extended Kalman filter estimates INS measurement errors, plus position, velocity and attitude errors etc. Kalman filter states, and gives precise navigation solutions while GPS signals are available. At the same time, a multi-layer neural network is trained to map the vehicle dynamics with corresponding Kalman filter states, at the same rate of measurement update. After the output of the neural network meets a similarity threshold, it can be used to correct INS measurements when no GPS measurements are available. Selecting suitable inputs and outputs of the neural network is critical for this hybrid method. Detailed analysis unveils that some Kalman filter states are highly correlated with vehicle dynamic variations. The filter states that heavily impact system navigation solutions are selected as the neural network outputs. The principle of this hybrid method and the neural network design are presented. Field test data are processed to evaluate the performance of the proposed method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.2
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• pp.57-65
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• 2008

Wireless sensor networks achieve environment monitoring and controlling through use of small devices of low cost and low power. Such network is comprised of several sensor nodes, each having a microprocessor, sensor, actuator and wired/wireless transceiver inside a small device. In this paper, we employ the sensor networks in order to design and implement a real-time vehicle safety system. Such system can inform the safe velocity in a specific weather condition to drivers in advance through analyzing the weather data collected from sensor networks. As a result, the drivers can prevent effectively accidents by controlling their car speed.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.6
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• pp.546-552
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• 2010

As the demands for the safety and convenience applications of the vehicles increase, the data load for the In-vehicle Network has increased significantly. As a result, a deterministic and fault-tolerant communication system is required to implement the safety critical applications such as X-by-wire systems. FlexRay communication system is a new standard of network communication system which provides the high speed serial communication, time triggered bus and fault tolerant communication between electronic devices. In addition to time-triggered communication, as providing of the event-triggered communication such as CAN, FlexRay protocol is able to manage the restricted communication resource more effectively. This paper presents the development of FlexRay driver which will be applied to the future ECU's communication system. To develop the FlexRay driver, the FlexRay requirement specification and FlexRay specification is analyzed. The developed FlexRay driver is implemented by using MPC5567 microprocessor of the Freescale semiconductor. To verify the developed FlexRay driver, the waveform of the FlexRay driver was measured and compared with the CAN communication system. As a result, the bus load is reduced about 13% compared with the CAN communication system.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.656-659
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• 1997

In order to improve vehicle safety, collision warning systems have been proposed by many researchers. This paper presents several algorithms to determine the degree of real end collision by using fuzzy logic and neural networks. In order to provide realistic data for the algorithm design, a data collection system has been installed on a passenger car.

• Journal of the Earthquake Engineering Society of Korea
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• v.10 no.6 s.52
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• pp.1-8
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• 2006

This paper describes the procedures developing the algorithm for analyzing signals acquired from the Bridge Weigh-in-Motion (BWIM) system installed in Seohae Bridge as a part of the bridge monitoring system. Through the analysis procedure, information about heavy traffics such as weight, speed, and number of axles are attempted to be extracted from time domain strain data of the BWIM system. One of numerous pattern recognition techniques, artificial neural network (ANN) is employed since it can effectively include dynamic effects, bridge-vehicle interaction, etc. A number of vehicle running experiments with sufficient load cases are executed to acquire training and/or test set of ANN. Extracted traffic information can be utilized for developing quantitative database of loading effect. Also, it can contribute to estimate fatigue lift or current health condition, and design truck can be revised based on the database reflecting recent trend of traffic.

• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.313-319
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• 2007

In this paper, we propose a novel method to extract an area of car licence plate and codes of vehicle number from a photographed car image using features on vertical edges and a new Fuzzy neural network algorithm to recognize extracted codes. Prewitt mask is used in searching for vertical edges for detection of an area of vehicle number plate and feature information of vehicle number palate is used to eliminate image noises and extract the plate area and individual codes of vehicle number. Finally, for recognition of extracted codes, we use the proposed Fuzzy neural network algorithm, in which FCM is used as the learning structure between input and middle layers and Max_Min neural network is used as the learning structure within inhibition and output layers. Through a variety of experiments using real 150 images of vehicle, we showed that the proposed method is more efficient than others.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.10
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• pp.17-26
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• 2010

This is concerned with the technology to monitor the vehicle operation, failure and disorder by using WiBro portable device. More precisely, the technology makes it possible that the information collection device is connected to both ECU(Electronic Control Unit) which is the device for controlling engine, transmission, brake, air-bag, etc that are connected to in-vehicle network and OBD-II connector that is for data collection from various sensors. In addition, with a WiBro portable device (cell phone, PDA, PMP, UMPC, etc). equipped with a vehicle diagnostic programs, information for operation, failure and malfunction can be obtained and analyzed in real-time, and alarm is alerted when the vehicle is in abnormal status, which makes the early reactions to the status. Furthermore, the collected data can be sent through WiBro network to the server managed by the company specialized in managing the vehicles, thus the technology could help the drivers who have less knowledge about their auto-vehicles have safe and economic driving. There is always a possibility of malfunction due to various types of noise that are caused by wring-harness when the device is wired-connected. In this research, in order to overcome this problem, we propose a system configuration that can do monitoring and diagnosis with a device for collecting data from vehicle and a personal WiBro device. Also, we performed research on data acquisition and interlock for the system defined by the definition for information and data sharing platform.