• 전자공학회논문지
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• 제52권10호
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• pp.47-55
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• 2015

전자 산업의 발달과 함께 많은 전자 제어 장치가 차량 내부에 탑재됨에 따라 차량의 기능을 검증하는 것은 더더욱 어려워지고 있다. 차량의 기능 오작동은 인명손실의 우려가 있기 때문에 차량에 있어서 신뢰성은 무엇보다 중요하게 고려되어야 한다. 본 논문에서는 차량의 신뢰성 향상을 위한 CAN 통신 기반의 고장 진단 플랫폼을 제안한다. 양산 이후에도 독립적인 테스트 경로를 통한 구조적 테스트를 실시함으로써 차량의 신뢰성은 크게 증가할 것이다. 또한, 별도의 테스트 핀이 필요하지 않기 때문에 테스트 비용을 절감할 수 있다.

• 한국인터넷방송통신학회논문지
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• 제15권6호
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• pp.79-88
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• 2015

본 논문에서는 최근 자동차 산업의 뜨거운 이슈로 부각되고 있는 이더넷 기반 차량 네트워크 기술을 살펴본다. 현재 인포테인먼트에 널리 쓰이는 MOST(Media Oriented Systems Transport)를 차량용 이더넷이 조만간 대치할 것으로 보이고 있다. 하지만 이더넷의 여러 가지 장점에도 불구하고 차량네트워크의 통합 백본으로 쓰기 위해서는 기존 표준 리거시 이더넷으로는 적합치가 않다. 따라서 브로드밴드 멀티미디어 트래픽뿐만 아니라 운전자 지원 및 안전 서비스 영역에서 요구하는 실시간성 및 신뢰성 요구조건에 맞도록 표준 리거시 이더넷을 확장 수정할 필요가 있으며 이를 위한 다양한 시도로써 타임-트리거드 이더넷(Time-triggered Ethernet)으로 알려져 있는 AS6802를 살펴보고, 트래픽 모델 해석적 성능 분석과 최악의 경우 지연시간 분석을 통해 차량네트워크로써 적합한 운용조건과 환경을 고찰한다.

• 한국멀티미디어학회논문지
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• 제19권12호
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• pp.1909-1918
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• 2016

This paper presents a unified framework for joint Convolutional Neural Network (CNN) based vehicle detection by leveraging multi-spectral image pairs. With the observation that under challenging environments such as night vision and limited light source, vehicle detection in a single color image can be more tractable by using additional far-infrared (FIR) image, we design joint CNN architecture for both RGB and FIR image pairs. We assume that a score map from joint CNN applied to overall image can be considered as confidence of vehicle existence. To deal with various scale ratios of vehicle candidates, multi-scale images are first generated scaling an image according to possible scale ratio of vehicles. The vehicle candidates are then detected on local maximal on each score maps. The generation of overlapped candidates is prevented with non-maximal suppression on multi-scale score maps. The experimental results show that our framework have superior performance than conventional methods with a joint framework of multi-spectral image pairs reducing false positive generated by conventional vehicle detection framework using only single color image.

• 대한임베디드공학회논문지
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• 제12권2호
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• pp.113-120
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• 2017

In this paper, we present a night-time vehicle detection method using CNN (Convolutional Neural Network) classification. The camera based night-time vehicle detection plays an important role on various advanced driver assistance systems (ADAS) such as automatic head-lamp control system. The method consists mainly of thresholding, labeling and classification steps. The classification step is implemented by existing CIFAR-10 model CNN. Through the simulations tested on real road video, we show that CNN classification is a good alternative for night-time vehicle detection.

• 한국자동차공학회논문집
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• 제12권4호
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• pp.110-118
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• 2004

In this paper, a blackbox approach is carried out to model the nonlinear dynamic bushing model. One-axis durability test is performed to describe the mechanical behavior of typical vehicle elastomeric components. The results of the tests are used to develop an empirical bushing model with an artificial neural network. The back propagation algorithm is used to obtain the weighting factor of the neural network. Since the output for a dynamic system depends on the histories of inputs and outputs, Narendra's algorithm of ‘NARMAX’ form is employed in the neural network bushing module. A numerical example is carried out to verify the developed bushing model.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.687-690
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• 2000

Lately, many studies have been progressed for the protection human's lives and property as holding in check accidents happened by human's carelessness or mistakes. One part of these is the development of an autonomouse vehicle. General control method of vision-based autonomous vehicle system is to determine the navigation direction by analyzing lane images from a camera, and to navigate using proper control algorithm. In this paper, characteristic points are abstracted from lane images using lane recognition algorithm with sobel operator. And then the vehicle is controlled using two proposed auto-steering algorithms. Two steering control algorithms are introduced in this paper. First method is to use the geometric relation of a camera. After transforming from an image coordinate to a vehicle coordinate, a steering angle is calculated using Ackermann angle. Second one is using a neural network algorithm. It doesn't need to use the geometric relation of a camera and is easy to apply a steering algorithm. In addition, It is a nearest algorithm for the driving style of human driver. Proposed controller is a multilayer neural network using Levenberg-Marquardt backpropagation learning algorithm which was estimated much better than other methods, i.e. Conjugate Gradient or Gradient Decent ones.

• 한국통신학회논문지
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• 제42권4호
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• pp.892-898
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• 2017

본 논문은 차량 내 AVN(Audio Video Navigation)에서 차량정보 분석과 제스처 인식이 가능한 소프트웨어 구조를 설계하고 구현 방법을 서술한다. 설계된 소프트웨어는 차량정보 분석을 위해 CAN(Controller Area Network) 통신 데이터 분석 모듈을 구현하여 차량의 주행 상태를 분석했다. AVN 소프트웨어는 분석된 정보를 웨어러블 디바이스의 제스처 정보와 융합토록 했다. 도출된 융합정보는 운전자의 명령 수행 단계로 매칭하고 서비스를 지원하는데 사용됐다. 설계된 AVN 소프트웨어는 기성 제품과 유사한 환경의 HW 플랫폼 상에 구현되어 차량 주행 상황과 동일하게 모사된 상황에서의 차량정보분석, 제스처 인식 수행 등의 기능을 지원함을 확인했다.

• 제어로봇시스템학회논문지
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• 제7권1호
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• pp.1205-1210
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• 2001

This paper describes a methodology using neural network to compensate the nonlinear error of deriving speed for electric differential system included in electric vehicle. An electric differential system which drives each of the left and right wheels of the electric vehicle independently. The electric vehicle driven by induction motor has the nonlinear speed error which depends on a steering angle and speed command. When a vehicle drives along a curved road lane, the speed unblance of inner and outer wheels makes vehicles vibration and speed reduction. To compensate for the speed error, we collected the speed data of the inner wheel and outer wheel in various speed and the steering angle data by using an manufactured electric vehicle and the real system. According to the analysis of the acquisited data, we designed the differential speed control system based on a speed error compensator using neural network.

• 전기학회논문지
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• 제66권9호
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• pp.1373-1378
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• 2017

This paper presents a basic ECU(Electronic Control Unit) hardware development procedure for the functional safety of in-vehicle network systems. We consider complete hardware redundancy as a safety mechanism for in-vehicle communication network under the assumption of the wired network failure such as disconnection of a CAN bus. An ESC (Electronic Stability Control) system is selected as an item and the required ASIL(Automotive Safety Integrity Level) for this item is assigned by performing the HARA(Hazard Analysis and Risk Assessment). The basic hardware architecture of the ESC system is designed with a microcontroller, passive components, and communication transceivers. The required ASIL for ESC system is shown to be satisfied with the designed safety mechanism by calculation of hardware architecture metrics such as the SPFM(Single Point Fault Metric) and the LFM(Latent Fault Metric).

• 한국지능시스템학회논문지
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• 제20권2호
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• pp.262-266
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• 2010

Recognizing a license plate of a vehicle has widely been issued. In this thesis, firstly, mean shift algorithm is used to filter and segment a color vehicle image in order to get candidate regions. These candidate regions are then analyzed and classified in order to decide whether a candidate region contains a license plate. We then present an approach to recognize a vehicle's license plate using the Fuzzy ARTMAP neural network, a relatively new architecture of the neural network family. We show that the proposed system is well to recognize the license plate and shows some compute simulations.