• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.4
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• pp.173-179
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• 2003

In this paper, a real-time multibody vehicle dynamics and control model has been developed for a virtual reality intelligent vehicle simulator. The simulator consists of low PCs for a virtual reality visualization system, vehicle dynamics and control analysis system a control loading system, and a network monitoring system. Virtual environment is created by 3D Studio Max graphic tool and OpenGVS real-time rendering library. A real-time vehicle dynamics and control model consists of a control module based on the sliding mode control for adaptive cruise control and a real-time multibody vehicle dynamics module based on the subsystem synthesis method. To verify the real-time capability of the model, cut-in, cut-out simulations have been carried out.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3672-3679
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• 2012

Vehicle Ad hoc Network (VANET) is next-generation network technology to provide various services using V2V (Vehicle-to-Vehicle) and V2I (Vehicle-to-Infrastructure). In VANET, many researchers proposed various studies for the safety of drivers. In particular, using the emergency message to increase the efficiency of traffic safety have been actively studied. In order to efficiently transmit to moving vehicle, to send a quick message to as many nodes is very important via broadcasting belong to communication range of vehicle nodes. However, existing studies have suggested a message for transmission to the communication node through indiscriminate broadcasting and broadcast storm problems, thereby decreasing the overall performance has caused the problem. In addition, theses problems has decreasing performance of overall network in various form of road and high density of vehicle node as urban area. Therefore, this paper proposed Clustering based Routing Algorithm (CBRA) to efficiently transmit emergency message in high density of vehicle as urban area. The CBRA managed moving vehicle via clustering when vehicle transmit emergency messages. In addition, we resolve linkage problem between vehicles according to various form of road. The CBRA resolve link brokage problem according to various form of road as urban using clustering. In addition, we resolve broadcasting storm problem and improving efficacy using selection flooding method. simulation results using ns-2 revealed that the proposed CBRA performs much better than the existing routing protocols.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2468-2478
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• 2018

We propose the Sparse Feature Convolutional Neural Network (SFCNN) to reduce the volume of convolutional neural networks (CNNs). Despite the superior classification performance of CNNs, their enormous network volume requires high computational cost and long processing time, making real-time applications such as online-training difficult. We propose an advanced network that reduces the volume of conventional CNNs by producing a region-based sparse feature map. To produce the sparse feature map, two complementary region-based value extraction methods, cluster max extraction and local value extraction, are proposed. Cluster max is selected as the main function based on experimental results. To evaluate SFCNN, we conduct an experiment with two conventional CNNs. The network trains 59 times faster and tests 81 times faster than the VGG network, with a 1.2% loss of accuracy in multi-class classification using the Caltech101 dataset. In vehicle classification using the GTI Vehicle Image Database, the network trains 88 times faster and tests 94 times faster than the conventional CNNs, with a 0.1% loss of accuracy.

• The KIPS Transactions:PartD
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• v.14D no.6
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• pp.649-656
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• 2007

Today, controller area network(CAN) is a field bus that is nowadays widespread in distributed embedded systems due to its electrical robustness, low price, and deterministic access delay. However, its use safety-critical applications has been controversial due to dependability limitation, such as those arising from its bus topology. Thus it is important to analyze the performance of the network in terms of load of data bus, maximum time delay, communication contention, and others during the design phase of the controller area network. In this paper, a simulation algorithm is introduced to evaluate the communication performance of the vehicle network and apply software base fault injection techniques. This can not only reduce any erratic implementation of the vehicle network but it also improves the reliability of the system.

• Journal of Positioning, Navigation, and Timing
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• v.7 no.1
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• pp.43-52
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• 2018

In recent years, the need of high accuracy navigation for vehicles has increased due to the development of autonomous driving vehicles and increase in land transportation convenience. This study is performed for vehicle users to achieve a performance of centimeter-level positioning accuracy by utilizing Compact Network Real-time Kinematic (RTK) that is applicable as a national-level infrastructure. To this end, medium-baseline RTK was implemented in real time to estimate accurate integer ambiguities between reference stations for reliable generation of Network RTK correction using the linear combination of carrier-phase observations and L1/L2 pseudo-range measurements. The residual tropospheric error was estimated in real time to improve the accuracy of double-differenced integer ambiguity resolution between network configuration reference stations that have at least 30 km or longer baseline distance. In addition, C++ based software was developed to enable real-time generation and broadcasting of Compact Network RTK correction information by utilizing an accurately estimated double-differenced integer ambiguity values. As a result, the horizontal and vertical 95% accuracy was 2.5cm and 5.2cm, respectively, without performance degradation due to user's position change within the network.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.3
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• pp.227-232
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• 2013

Due to the development of ITS technology, various services related to transportation under vehicular environments have been provided. Especially, as wireless communication technology, WAVE has been established as a standard for vehicle-to-vehicle communications. WAVE has fast connection and excellent mobility characteristics. A VSC-A project is conducting by global automotive OEMs in USDOT. This project introduces the advanced safety services with vehicle-to-vehicle communications. In this paper, we presented the scenario of a do not pass warning service, which prevents an accident during overtaking activity by using vehicle-to-vehicle communications. In addition, we analyzed network performance under WAVE. In conclusion, we introduced the simulation results. Finally, we summarized the communication range and delay values for consideration factors for a overtaking model.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.4
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• pp.43-50
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• 2010

Vehicle-to-Vehicle (V2V) is a special type of vehicle ad-hoc network (VANET), and known as a solution to provide communication among vehicles and reduce vehicle accidents. Geographical routing protocols as Greedy Perimeter Sateless Routing (GPSR) are very suitable for the V2V communication due to special characters of highway and device for vehicles. However, the GPSR has problem that appears local maximum by some stale neighbor nodes in the greedy mode of the GPSR. It can lose transmission data in recovery mode, even if the problem is can be solved by the recovery mode of the GPSR. We therefore propose a Greedy Perimeter Reliable Routing (GPRR), can provide more reliable data transmission, to resolve the GPSR problem in the V2V environment. Simulation results using ns-2 shown that the GPRR reveals much better performance than the GPSR by remarkably reducing the local maximum rate in the greedy mode.

• 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 Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.731-734
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• 2009

In this paper, we propose the method the vehicle color recognizing in the image including a vehicle. In an image, the color feature vector of a vehicle is extracted and by using the backpropagation learning algorithm, that is the multi-layer perceptron, the recognized vehicle color. By using the RGB and HSI color model the feature vector used as the input of the backpropagation learning algorithm is the feature of the color used as the input of the neural network. The color of a vehicle recognizes as the white, the silver color, the black, the red, the yellow, the blue, and the green among the color of the vehicle most very much found out as 7 colors. By using the image including a vehicle for the performance evaluation of the method proposing, the color recognition performance was experimented.

• Journal of Korean Society of Transportation
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• v.25 no.2 s.95
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• pp.133-143
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• 2007

Previous studies on the transit assignment hardly show its achievement in research but have many limitations not only in theory but also in practice. This paper presents an integrated transit assignment model taking into account cost functions of multiple modes, such as auto, bus and subway, which represent an integrated network. An integrated transit network including cost functions and in-vehicle congestion needs to be developed. In addition, a link fare calculation model needs to be developed and applied to the model to calculate path travel costs. Based on these sub-models, a path-based traffic assignment model, which considers in-vehicle congestion and an integrated distance-based fare system in the integrated traffic network, is developed.