• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2009년도 추계학술대회
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• pp.82-85
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• 2009

최근 편의성, 안전성, 편리성 등의 키워드가 자동차 시장에서 새로운 화두로 등장하면서 자동차 시장에서 차량 내 전장부분의 중요성이 커지고 있다. 이에 따라 많은 전자 기기의 사용이 필수적으로 요구되어지면서 전자 기기들 간의 통신이 부각 되어지고 있다. 차량 내부에서는 컨트롤러, 센서, 그리고 멀티미디어 기기인 오디오, 스피커, 비디오, 내비게이션 등 다양한 장치들이 CAN 이나 MOST와 같은 차량 네트워크를 통해 연결 되어 있다. 현재 차량 네트워크는 서로 각각의 목적에 따라 운용 되고 관리 되어 지고 있다. 본 논문에서는 MOST Network를 이용하여 최근의 키워드가 되고 있는 편의성, 안전성, 편리성 등을 고려한 지능형 자동차에 요구되는 Navigation을 구현하여 차량 내 CAN Network를 제어하는 시스템을 제시하고자 한다.

• 전기전자학회논문지
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• 제22권2호
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• pp.518-521
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• 2018

차량 내부의 다양한 전자장치를 연결하는 차량 내 통신(IVN: in-vehicle network)은 실시간성, 저잡음성, 고신뢰성, 고유연성 등이 필요하며 CAN(controller area network), CAN-FD(CAN flexible data rate), FlexRay, LIN(local interconnect network), SENT(single edge nibble transmission), PSI5(peripheral sensor interface 5) 등 다양한 기술이 있다. 본 논문에서는 이들 기술의 동작 원리에 대해 살펴보고 각 기술의 적용 대상과 장단점에 대해 설명한다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2010년도 추계학술대회
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• pp.66-69
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• 2010

최근 차량에는 차량과 IT와 결합을 차량의 안전성, 편리성을 위해 많은 전자제어장치(ECU)가 장착되어 있고 있다. 이러한 전자제어장치는 각 전자제어장치에 대한 Data는 OBDII 네트워크에 전송을 하고 멀티미디어에 대한 Data 는 MOST 네트워크에 전송을 한다. 본 논문에서는 기존의 블랙박스의 문제점을 보완하기 위해 차량용 네트워크의 새로운 멀티미디어 네트워크인 MOST 차량용 네트워크와 현재 차량에서 표준적으로 쓰이는 OBDII 차량용 네트워크를 이용하여 전자제어장치의 데이터를 이용한 Data들을 취합하여 차량의 현재 상황을 판단하고 정보를 제공함으로써 차량의 안전성 및 블랙박스로써의 역할을 극대화 시킬 수 있는 차량용 블랙박스 시스템을 구현하고자 한다.

• 제어로봇시스템학회논문지
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• 제8권12호
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• pp.1023-1030
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• 2002

Since the dynamics of autonomous underwater vehicles (AUVs) are highly nonlinear and their hydrodynamic coefficients vary with different vehicle's operating conditions, high performance control systems of AUVs are needed to have the capacities of teaming and adapting to the variations of the vehicle's dynamics. In this paper, a linearly parameterized neural network (LPNN) is used to approximate the uncertainties of the vehicle dynamics, where the basis function vector of the network is constructed according to the vehicle's physical properties. The network's reconstruction errors and the disturbances in the vehicle dynamics are assumed be bounded although the bound may be unknown. To attenuate this unknown bounded uncertainty, a certain estimation scheme for this unknown bound is introduced combined with a sliding mode scheme. The proposed controller is proven to guarantee that all signals in the closed-loop system are uniformly ultimately bounded (UUB). Numerical simulation studies are performed to illustrate the effectiveness of the proposed control scheme.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2005년도 추계종합학술대회
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• pp.1023-1026
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• 2005

In the ubiquitous computing environment, an intelligent vehicle is defined as a sensor node with a capability of intelligence and communication in a wire and wireless network space. To make it real, a lot of problems should be addressed in the aspect of vehicle mobility, in-vehicle communication, common service platform and the connection of heterogeneous networks to provide a driver with several intelligent information services beyond the time and space. In this paper, we present an intelligent information system for managing in-vehicle sensor network and a vehicle gateway for connecting the external networks. The in-vehicle sensor network connected with several sensor nodes is used to collect sensor data and control the vehicle based on CAN protocol. Each sensor node is equipped with a reusable modular node architecture, which contains a common CAN stack, a message manager and an event handler. The vehicle gateway makes vehicle control and diagnosis from a remote host possible by connecting the in-vehicle sensor network with an external network. Specifically, it gives an access to the external mobile communication network such as CDMA. Some experiments was made to find out how long it takes to communicate between a vehicle's intelligent information system and an external server in the various environment. The results show that the average response time amounts to 776ms at fixed place, 707ms at rural area and 910ms at urban area.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.281-286
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• 2000

This paper describes a lateral guidance system of an unmanned 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 the unmanned vehicle 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 learning pattern, learning itself, and the adequacy of the design controller. A computer simulation of the vehicle (including vehicle dynamics and steering) was used to verify the steering performance of the vehicle controller using the neural network. Good results were obtained. Also, the real unmanned electrical vehicle using neural network controller verified good results.

• 전기학회논문지
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• 제64권10호
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• pp.1448-1453
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• 2015

In this paper, we propose an alternative method of communication to improve the reliability of in-vehicle networks by jointly using wireless communication networks. Wired Communication networks have been used in vehicles for the monitoring and the control of vehicle motion, however, the disconnection of wires or hardware fault of networks may cause a critical problem in vehicles. If the network manager detects a disconnection or faults in wired in-vehicle network like the Controller Area Network(CAN), it can redirect the communication path from the wired to the wireless communication like the Zigbee network. To show the validity and the effectiveness of the proposed in-vehicle network architecture, we implement the Electronic Stability Control(ESC) system as ECU-In-the-Loop Simulation(EILS) and verify that the control performance can be kept well even if some hardware faults like disconnection of wires occur.

• 제어로봇시스템학회논문지
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• 제5권5호
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• pp.550-557
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• 1999

This paper describes a road-following controller using the proposed neural network for autonomous vehicle. Road-following with visual sensor like camera requires intelligent control algorithm because analysis of relation from road image to steering control is complex. The proposed neural network, relative similarity modular network(RSMN), is composed of some learning networks and a partitioniing network. The partitioning network divides input space into multiple sections by similarity of input data. Because divided section has simlar input patterns, RSMN can learn nonlinear relation such as road-following with visual control easily. Visual control uses two criteria on road image from camera; one is position of vanishing point of road, the other is slope of vanishing line of road. The controller using neural network has input of two criteria and output of steering angle. To confirm performance of the proposed neural network controller, a software is developed to simulate vehicle dynamics, camera image generation, visual control, and road-following. Also, prototype autonomous electric vehicle is developed, and usefulness of the controller is verified by physical driving test.

• 한국정밀공학회지
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• 제19권9호
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• pp.51-58
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• 2002

Vehicle dynamics control systems are. complex and non-linear, so they have difficulties in developing a controller for the anti-lock braking systems and the auto-traction systems. Currently the fuzzy-logic technique to estimate the absolute vehicle speed is good results in normal conditions. But the estimation error in severe braking is discontented. In this paper, we estimate the absolute vehicle speed by using the wheel speed data from standard 50-tooth anti-lock braking system wheel speed sensors. Radial symmetric basis function of the neural network model is proposed to implement and estimate the absolute vehicle speed, and principal component analysis on input data is used. Ten algorithms are verified experimentally to estimate the absolute vehicle speed and one of those is perfectly shown to estimate the vehicle speed with a 4% error during a braking maneuver.

• 한국산학기술학회논문지
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• 제18권5호
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• pp.125-132
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• 2017

차량 안전 서비스는 무선 환경에서 차량 간 통신(Vehicle-to-Vehicle, V2V)을 통하여 운전 중 발생할 수 있는 위험을 사전에 감지하여 운전자에게 알려줌으로써 교통사고와 교통 체증을 줄이는 서비스이다. 차량 안전 서비스를 위해서 차량은 안전 메시지(Basic Safety Message, BSM)를 주기적으로 브로드캐스트 한다. 하지만, 차량 밀집 지역에서 이는 과도한 네트워크 트래픽의 원인이 되고 안전 메시지의 전송실패 확률과 지연을 급격하게 증가시켜 차량 안전 서비스의 안정성을 떨어뜨린다. 본 논문에서는 차량 안전 서비스를 수행하는 과정에서 발생하는 통신 혼잡 문제를 해결하기 위해 Channel Busy Ratio와 차량 수 간의 관계를 수식적으로 근사하고 이를 이용하여 전체 네트워크 혼잡도를 추정한다. 그리고 이를 기준으로 송신전력을 제어하는 새로운 상황인지 기반 송신전력제어 알고리즘을 제안한다. 제안하는 알고리즘은 네트워크 시뮬레이터인 Qualnet을 이용하여 성능평가를 하였고 그 결과, 전체 네트워크 혼잡도의 추정이 특정 시나리오에서 정확하게 근사 되었으며 송신전력제어를 통해 차량 안전 통신 간 패킷 에러율이 감소한 것을 확인할 수 있었다.