• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.47-55
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• 2015

To verify the function of vehicle is becoming more and more difficult because many electronic control units have been embedded in vehicle with development of electronics industry. The reliability of vehicle should be considered above all important because malfunction of vehicle can cause damage of human life. In this paper, defect diagnosis platform based on CAN network is proposed to improve the reliability of vehicle. Reliability of vehicle is significantly increased by adopting the structural test via dedicated test path after manufacturing. Besides, the test cost is reduced because additional test pins are not required.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.79-88
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• 2015

In this paper, we examine Ethernet based vehicle network which is recently emerging technology. Current MOST for entertainment will be soon replaced with the emerging Ethernet based vehicle network. Although legacy standard Ethernet has several advantages it is not suitable for vehicle backbone network without any modification. As a result, many researches are happening on extending and modification of the Ethernet function for realtime and reliability. Time-triggered Ethernet, one of many trials known as AS6802, is investigated on the architecture and functionalities. We design the traffic model on Time-triggered Ethernet and analyse the latency of the network. We also consider the QoS requirement and environment of operating configuration for vehicle network.

• Journal of Korea Multimedia Society
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• v.19 no.12
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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.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.2
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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.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.4
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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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.4
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• pp.892-898
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• 2017

This paper describes the development of AVN(Audio Video Navigation) software for vehicle information analysis and gesture recognition. The module that examine the CAN(Controller Area Network) data of vehicle in the designed software analyzes the driving state. Using classified information, the AVN software converge vehicle information and hand gesture information. As the result, the derived data is used to match the service step and to perform the service. The designed AVN software was implemented in HW platform that common used in vehicles. And we confirmed the operation of vehicle analysing module and gesture recognition in a simulated environment that is similar with real world.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.1
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.9
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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).

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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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.