• 한국정보기술학회 영문논문지
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• 제10권1호
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• pp.15-23
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• 2020

In this paper, we propose a new method to detect traffic accidents in video from vehicle-mounted cameras (vehicle black box). We use the distance between vehicles to determine whether an accident has occurred. To calculate the position of each vehicle, we use object detection and tracking method. By the way, in a crowded road environment, it is so difficult to decide an accident has occurred because of parked vehicles at the edge of the road. It is not easy to discriminate against accidents from non-accidents because a moving vehicle and a stopped vehicle are mixed on a regular downtown road. In this paper, we try to increase the accuracy of the vehicle accident detection by using not only the motion of the surrounding vehicle but also ego-motion as the input of the Recurrent Neural Network (RNN). We improved the accuracy of accident detection compared to the previous method.

• 전기학회논문지
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• 제61권6호
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• pp.859-864
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• 2012

The computer network plays an important role in modern digital controllers within a safety system of a nuclear power plant. For the reliable and realtime data communication between controllers, this paper proposes a modified high-reliable MVB(multi-function vehicle bus) as a main control network for a safety system of a nuclear power plant. The proposed network supports the state-based communication in order to ensure the deterministic communication latency, and very fast network recovery when the bus master fails compare to the standard MVB. This paper also shows the implementation results using a FPGA-based testbed.

• 경영과학
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• 제28권3호
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• pp.1-13
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• 2011

Hub-and-spoke transportation network is a powerful and useful network structure that takes full advantage of economies of scale on routes between hubs. In recent studies, the network structure is extended to hybrid hub-andspoke that allows direct transportation between spokes. In this study, we considered more extended network structure which is called hybrid multiple hub-and-spoke that has multiple hubs and allows direct transportation between spokes. We developed a mathematical optimization model for automotive service parts transportation planning under hybrid multiple hub-and-spoke network structure. The model suggests a long-term transportation route planning and a short-term vehicle assignment planning. The model is verified by simulation and validated in real world application to Hyundai Mobis automotive service parts transportation planning. From the simulation result, the model reduced the transportation cost about 24.7%, the total distance about 6.8% and the CO2 emissions about 8.8%. In real world application for 6 months from July to December 2010, the model reduced the transportation cost about 9.1% by changing the long-term transportation route without daily vehicle assignment planning.

• 한국정보통신학회논문지
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• 제14권4호
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• pp.901-908
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• 2010

자동차 내에는 그 용도에 따라 다양한 네트워크가 존재한다. 그러나 현재 제공되고 있는 차량내 네트워크 기술은 하나의 네트워크로 통합되어 운용되는 것이 아니라, 통신 속도와 비용 및 효율성 측면을 고려하여 필요에 따라 서 다르게 구성이 되어 상용화 되어 있다. 따라서 각 네트워크간의 정보 교환을 위한 차량 네트워크 통신 및 설계의 복잡성이 증대 되었고, 이에 따라 지능형 교통 시스템을 위한 이들 네트워크를 연동할 수 있는 프레임워크가 반드시 필요하다. 따라서 본 논문에서는 지능형 교통 시스템을 위한 이기종 차량 네트워크의 연동을 위한 프레임워크를 제안하고, 구현하였다. 제안한 프레임워크는 호환 가능한 프로토콜과 메시지변환, 송수신, 분석모듈로 구성된다. 구현결과 프레임워크는 이기종 차내 네트워크간의 정보교환이 원활함을 보여주었다.

• 정보보호학회논문지
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• 제22권5호
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• pp.1057-1068
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• 2012

차량 기술이 발전함에 따라 차량 내부에는 많은 수의 ECU(Electronic Control Unit)가 탑재되고 있다. 차량 내부에 탑재된 ECU간의 통신은 CAN(Controller Area Network)을 통해 이루어진다. CAN은 높은 신뢰성을 갖기 때문에 안전한 차량통신을 지원한다. 하지만 별도의 보안메커니즘이 적용되어 있지 않기 때문에, 많은 취약점을 내포하고 있다. 최근 연구에서는 CAN의 취약점을 이용한 공격이 제시되고 있다. 본 논문에서는 이동 통신망을 이용한 차량 내부 네트워크에 대한 원격공격 모델을 제시한다. 또한 차량 내부 메시지의 기밀성과, 무결성을 보장하면서 동시에 리플레이 공격을 방지할 수 있는 안전한 차량 내부 네트워크 메시지 인증 메커니즘을 제시한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권12호
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• pp.5878-5904
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• 2017

This paper proposes a novel intelligent wheelchair vehicle system that enables upper limb exercises, lower limb standing exercises and rehabilitation training in a daily life. The proposed system, which can be used to prevent at least the degeneration of body movements and further atrophy of musculoskeletal system functions, considers the characteristics and mobility of the old and the disabled. Its main purpose is to help the old and the disabled perform their daily activities as much as they can, minimizing the extent of secondary disabilities. In other words, the system will provide the old and the disabled with regular and quantitative rehabilitation exercises and diagnosis using the wheelchair-based upper/lower limb rehabilitation vehicle system and then verify their effectiveness. The system comprises an electric wheelchair, a biometric module to identify individual characteristics, and an upper/lower limb rehabilitation vehicle. In this paper the design and configuration of the developed vehicle is described, and its operation method is presented. Moreover, to verify the tracking performance of the proposed system, dangerous situations according to biosignal changes occurring during the rehabilitation exercise of a non-disabled examinee are analyzed and the performance of the upper/lower limb rehabilitation exercise function depending on muscle strength is evaluated through a neural network algorithm.

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.67-72
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• 2023

In the past decade, Autonomous Vehicle Systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as well as road safety and the future of transportation systems. The fusion of light detection and ranging (LiDAR) and camera data in real-time is known to be a crucial process in many applications, such as in autonomous driving, industrial automation and robotics. Especially in the case of autonomous vehicles, the efficient fusion of data from these two types of sensors is important to enabling the depth of objects as well as the classification of objects at short and long distances. This paper presents classification of objects using CNN based vision and Light Detection and Ranging (LIDAR) fusion in autonomous vehicles in the environment. This method is based on convolutional neural network (CNN) and image up sampling theory. By creating a point cloud of LIDAR data up sampling and converting into pixel-level depth information, depth information is connected with Red Green Blue data and fed into a deep CNN. The proposed method can obtain informative feature representation for object classification in autonomous vehicle environment using the integrated vision and LIDAR data. This method is adopted to guarantee both object classification accuracy and minimal loss. Experimental results show the effectiveness and efficiency of presented approach for objects classification.

• 산업공학
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• 제11권1호
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• pp.75-84
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• 1998

The important issue for intra-city vehicle scheduling is to measure and store actual vehicle travel speeds between customer locations. Travel speeds(and times) in nearly all metropolitan areas change drastically during the day because of congestion in certain parts of the city road network. We propose three models for estimating departure time-dependent travel speeds between locations that relieve much burden for the data collection and computer storage requirements. Two of the three models use a least squares method and the rest one employs a neural network trained with the back-propagation rule. On a real-world study using the travel speed data collected in Seoul, we found out that the neural network model is more accurate than the other two models.

• 한국인터넷방송통신학회논문지
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• 제17권6호
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• pp.127-135
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• 2017

대부분의 최신 자동차들은 편안하고 안전한 운전 환경을 위해 다양한 종류의 ECU들을 탑재하고 있다. ECU들 사이의 효율적인 통신을 위해 대부분의 자동차 제조사들은 Controller Area Network(CAN) 프로토콜을 사용하고 있다. 그러나 CAN은 데이터 인증을 제공하지 않는다. 이러한 취약점 때문에 CAN은 메시지 재생공격에 취약하다. 본 논문은 자동차 내부 네트워크에 적용 가능한 현실적인 메시지 인증 기법을 제안한다. CAN 데이터 프레임의 제한적인 공간을 고려하여, 데이터와 메시지 인증 코드 (MAC)를 동시에 전송하기 위해서는 짧은 길이의 MAC을 사용하는 것이 가장 적합하다. 그러나 짧은 길이의 MAC은 암호학적 안전성을 충분히 보장하지 않기 때문에 안전성을 보장하기 위한 추가적인 조치가 필요하다. 본 연구에서 제안한 메시지 인증 기술은 CAN의 제한된 데이터 페이로드를 고려하기 때문에 차량 내부의 안전한 네트워크를 설치하는데 유용하게 활용될 수가 있다.

• 한국자동차공학회논문집
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• 제8권6호
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• pp.142-155
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• 2000

A MIMO model reference control scheme incorporating the variable structure theory for a vehicle four wheel steering system(4WS) is proposed and evaluated for a class of continuous-time nonlinear dynamics with known or unknown uncertainties. The scheme employs an neural network to identify the plant systems, where the neural network estimates the nonlinear dynamics of the plant. By the Lyapunov direct method, the algorithm is proven to be globally stable, with tracking errors converging to the neighborhood of zero. The merits of this scheme is that the global system stability is guaranteed and it is not necessary to know the exact structure of the system. With the resulting identification model which contains the neural networks, it does not need higher degrees of freedom vehicle model than 3 degree of freedom model. Th proposed scheme is applied to the active four wheel system and shows the validity is used to investigate vehicle handing performances. In simulation of the J-turn maneuver, the reduction of yaw rate overshoot of a typical mid-size car improved by 30% compared to a two wheel steering system(2WS) case, resulting that the proposed scheme gives faster yaw rate response and smaller side angle than the 2WS case.