• Journal of information and communication convergence engineering
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• 제8권5호
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• pp.554-559
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• 2010

Recently, various in-vehicle networks have been developed respectively in order to accomplish their own purpose such as CAN and MOST. Various electronic devices for vehicle are controllable by the advent of networks attached to the vehicle. However, the networks also come with a variety of unique features in each network-specific communication which creates difficulty using and supporting the interoperable services among the networks. To solve this problem, each network needs a standard integration framework. In this paper, a framework is proposed and implemented. It consists of a standard protocol using XML to improve compatibility and portability. The framework makes each network interoperable by applying unique information and messages of the network in the XML standard document. The results obtained by implementation show that the framework supports the efficient communication of data between heterogeneous invehicle networks.

• 융합보안논문지
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• 제16권6_2호
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• pp.99-107
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• 2016

이 연구의 목적은 Bluetooth를 활용한 자동차의 내부망 통신네트워크를 해킹공격으로부터 예방하기 위한 대응방안을 제시하기 위함이다. 이를 위해 2장에서는 자동차 통신네트워크의 정의와 내부망 통신네트워크의 종류에 대해서 살펴보았다. 3장에서는 자동차 내부망 통신네트워크 해킹위험성을 분석하기 위해 Bluetooth에 의한 해킹범죄사례를 살펴보았다. 4장에서는 본 연구의 결과로서 첫째, "자동차안전기준에 관한 규칙" 개정이 이루어져야 한다. 동법에서는 전자제어시스템의 정의 및 기준사항과 안전운행을 위한 제작 및 정비를 규정해 놓았음에도 불구하고 전자제어시스템을 대상으로 한 해킹범죄 예방 및 방어와 관련된 보안프로그램 혹은 펌웨어 등의 제작과 관련된 규정이 없는 실정이다. 둘째, 자동차의 전자제어시스템 기술의 복잡성에 기인된 자동차 통신네트워크를 보호하고자 자동차 통신네트워크 보호 법률이 신설되어야 한다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.133-139
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• 1997

This paper presents a new approach to the dynamic control technique for track vehicle system using neural network-fuzzy control method. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is propored a learning controller consisting of two neural network-fuzzy based on independent resoning and a connection net with fixed weights to simply the neural network-fuzzy. The performance of the proposed controller is shown by simulation for trajectory tracking of the speed and azimuth of a track vehicle

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.196-196
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• 2000

This paper describes a lateral guidance system of an autonomous vehicle, using a neural network model of magneto-resistive sensor and magnetic fields. The model equation was compared with experimental sensing data. We found that the experimental result has a negligible difference from the modeling equation result. We verified that the modeling equation can be used in simulations. As the neural network controller acquires magnetic field values(B$\_$x/, B$\_$y/, B$\_$z/) from the three-axis, the controller outputs a steering angle. The controller uses the back-propagation algorithms of neural network. The learning pattern acquisition was obtained using computer simulation, which is more exact than human driving. The simulation program was developed in order to verify the acquisition of the teaming pattern, teaming itself, and the adequacy of the design controller. The performance of the controller can be verified through simulation. The real autonomous electric vehicle using neural network controller verified good results.

• 제어로봇시스템학회논문지
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• 제16권10호
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• pp.1018-1023
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• 2010

The CAN (Controller Area Network) system is the most dominant protocol for in-vehicle networking system because it provides bounded transmission delay among ECUs (Electronic Control Units) at data rates between 125Kbps and 1Mbps. And, many automotive companies have chosen the CAN protocol for their in-vehicle networking system such as chassis network system because of its excellent communication characteristics. However, the increasing number of ECUs and the need for more intelligent functions such as ADASs (Advanced Driver Assistance Systems) or IVISs (In-Vehicle Information Systems) require a network with more network capacity and the real-time QoS (Quality-of-Service). As one approach to enhancing the network capacity of a CAN system, this paper introduces a CAN system with dual communication channel. And, this paper presents a distributed message allocation method that allocates messages to the more appropriate channel using forecast traffic of each channel. Finally, an experimental testbed using commercial off-the-shelf microcontrollers with two CAN protocol controllers was used to demonstrate the feasibility of the CAN system with dual communication channel using the distributed message allocation method.

• 디지털콘텐츠학회 논문지
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• 제8권3호
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• pp.371-376
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• 2007

차량 내부에서는 컨트롤러, 센서, 텔레매틱스 단말기, 내비게이션, 오디오 및 비디오 등 다양한 모듈들이 CAN이나 MOST와 같은 차량 네트워크를 통해 연결되어 있다. 게다가, 사용자는 이동 중에 무선 모바일 네트워크를 이용하여 ITS나 인터넷에 접속할 수도 있다. 이러한 네트워크의 다양한 활용은 데이터 해킹, 프라이버시 침해, 위치 추적 등과 같은 많은 보안 문제를 야기하게 된다. 또한, 차량 운영 데이터(센서, 제어 데이터)를 해킹함으로써 차량을 고장 내거나 사고를 유발할 수 있는 가능성도 점차 커지고 있다. 본 논문에서는 CAN이나 MOST와 같은 차량 네트워크에 적용할 수 있는 암호화 기능을 가지는 보안 모듈을 제안한다. 이 보안 모듈은 DES, 3-DES, SEED, ECC 및 RSA와 같은 일반적인 암호화 알고리듬과 전자서명 기능을 제공하게 된다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2009년도 추계학술대회
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• pp.90-93
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• 2009

최근 차량용 네트워크 시스템으로 사용되고 있는 CAN(Controller Area Network)은 많은 ECU들이 필요한 미래형 스마트차량에 적합한 네트워크 프로토콜로서 안정성과 신뢰성을 보장해주며, 많은 ECU들의 장착으로 Wiring Harness의 공간과 중량이 늘어남으로 인해 발생되는 에너지 소비와 비용의 증가를 대폭 줄일 수 있는 것으로 나타났다. 본 논문에서는 CAN프로토콜을 이용하여 미래형 스마트 자동차에 요구되는 편의주행, 쾌적주행을 위해 Air conditioner 와 Heater를 제어하여 차량 내부 온도를 운전자의 요구에 맞도록 자동으로 제어할 수 있는 시스템을 구현하고자 한다.

• International Journal of Automotive Technology
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• 제3권2호
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• pp.71-78
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• 2002

A new control method far vision-based autonomous vehicle is proposed to determine navigation direction by analyzing lane information from a camera and to navigate a vehicle. In this paper, characteristic featured data points are extracted from lane images using a lane recognition algorithm. Then the vehicle is controlled using new Levenberg-Marquardt neural network algorithm. To verify the usefulness of the algorithm, another algorithm, which utilizes the geometric relation of a camera and vehicle, is introduced. The second one involves transformation from an image coordinate to a vehicle coordinate, then steering is determined from Ackermann angle. The steering scheme using Ackermann angle is heavily depends on the correct geometric data of a vehicle and a camera. Meanwhile, the proposed neural network algorithm does not need geometric relations and it depends on the driving style of human driver. The proposed method is superior than other referenced neural network algorithms such as conjugate gradient method or gradient decent one in autonomous lateral control .

• Steel and Composite Structures
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• 제46권6호
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• pp.731-744
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• 2023

Vehicle load information is an important role in operating and ensuring the structural health of cable-stayed bridges. In this regard, an efficient and economic method is proposed for vehicle load detection based on the observed cable tension and vehicle position using a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), a robust program for modeling and considering both geometric and material nonlinearities of bridge structures subjected to vehicle load with low computational costs. With the superiority of GNN, the proposed model is demonstrated to precisely capture complex nonlinear correlations between the input features and vehicle load in the output. Four popular machine learning methods including artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machines (SVM) are refereed in a comparison. A case study of a cable-stayed bridge with the typical truck is considered to evaluate the model's performance. The results demonstrate that the GNN-based model provides high accuracy and efficiency in prediction with satisfactory correlation coefficients, efficient determination values, and very small errors; and is a novel approach for vehicle load detection with the input data of the existing monitoring system.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2003년도 추계학술대회 논문집(III)
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• pp.204-209
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• 2003

Recently, The TCN(Train Communication Network} as the distributed control systems for electric vehicles, which is the international standard of the intra vehicle communication, actively recognizes variations and supports reconfiguration of the train network when a vehicle is separated or recombined. The technique of reconfiguration to take variety and interoperability of a vehicle constitution is used when the vehicle constitution is changed. At the time, each node making up vehicle network shares the information about the variation of vehicle constitutions and the state of nodes. In the hierarchical TCN structure, an exchange of data becomes available as a work to transmit information between components is performed at the node playing a role of gateway. This paper proposes a protocol to transmit the information of the train reconfiguration. The protocol gives an application to renew a list for transmitting information and to perform the transmission that can guarantee periodic and non-periodic data transmission between nodes when the network nodes changed by a variation of the network state are reconfigured. If use this protocol, can use functions that are offered in the electric railcar at the same time that composition of vehicles is completed without delay. And when driver of the electric railcar inspect before running of vehicles, can confirm state of vehicles visually through monitor in driver's room.