• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.3
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• pp.132-138
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• 2011

This paper presents an intelligent control approach for lateral position control of an autonomous four wheel steered snowplowing robotic vehicle. The vehicle is built for removing snow on the highway. Dynamics of the vehicle is derived and linearized for LQR control. Lateral position is controlled by the LQR method first, then the neural network control technique is introduced to improve tracking performances under the presence of load. The feasibility of using four wheel steering control is investigated by simulation studies of lateral position tracking of the Ford F-250 truck model. Performances of a LQR control method and a neural network control method under virtual snowplowing situation are compared.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.68-79
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• 2021

On the Social Internet of Things (SIoT), social activities occur through which the vehicle generates a variety of data, shares them with other vehicles, and sends and receives feedbacks. In order to share reliable information between vehicles, it is important to determine the reliability of a vehicle. In this paper, we propose a vehicle trust evaluation scheme to share reliable information among vehicles. The proposed scheme calculates vehicle trust by considering user reputation and network trust based on inter-vehicle social behaviors. The vehicle may choose to scoring, ignoring, redistributing, etc. in the social activities inter vehicles. Thereby, calculating the user's reputation. To calculate network trust, distance from other vehicles and packet transmission rate are used. Using user reputation and network trust, local trust is calculated. It also prevents redundant distribution of data delivered during social activities. Data from the Road Side Unit (RSU) can be used to overcome local data limitations and global data can be used to calculate a vehicle trust more accurately. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.663-672
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• 2023

Most vehicle detection methods have poor vehicle feature extraction performance at night, and their robustness is reduced; hence, this study proposes a night vehicle detection method based on style transfer image enhancement. First, a style transfer model is constructed using cycle generative adversarial networks (cycleGANs). The daytime data in the BDD100K dataset were converted into nighttime data to form a style dataset. The dataset was then divided using its labels. Finally, based on a YOLOv5s network, a nighttime vehicle image is detected for the reliable recognition of vehicle information in a complex environment. The experimental results of the proposed method based on the BDD100K dataset show that the transferred night vehicle images are clear and meet the requirements. The precision, recall, mAP@.5, and mAP@.5:.95 reached 0.696, 0.292, 0.761, and 0.454, respectively.

• 전자공학회논문지 IE
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• v.48 no.2
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• pp.19-25
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• 2011

This paper presented neural network modeling method using vehicle data to predict fuel consumption. To acquire data for training and testing the proposed neural network, medium-class gasoline vehicle drove at downtown and parameters measured include speed, engine rpm, throttle position sensor (TPS), and mass air flow (MAF) as input data, and fuel consumption as target data from OBD-II port. Multi layer perception network was used for nonlinear mapping between the input and the output data. It was observed that the neural network model can predict the vehicle quite well with mean squared error was $1.306{\times}10^{-6}$ for the fuel consumption.

• Journal of IKEEE
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• v.24 no.4
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• pp.1184-1187
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• 2020

This paper proposes an in-vehicle network controller fully exploiting the advantages of UART (Universal Asynchronous Receiver/Transmitter) and CAN (Controller Area Network). UART is used in 1-to-1 communication and it exploits parity bit for data integrity check. The proposed in-vehicle network controller converts UART into CAN, which enables multiple communications along with 1-to-1 communication. Also, the proposed in-vehicle network controller exploits CRC (cyclic redundancy check) for data integrity check, which increases communication reliability. CAN is controlled by microprocessor, but the proposed in-vehicle network controller can be controlled by any devices compliant with RS-232, RS-422, and RS-485.

• Journal of Ocean Engineering and Technology
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• v.11 no.4
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• pp.180-188
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• 1997

This paper proposes a new efficient MAC(Media Access Control) protocol to establish the ultrasonic communication network for underwater vehicles, which ensures a certain level of maximum throughput regardless of the propagation delay of ultrasonic and allows fast data transmission through the multiple ultrasonic communication channel. A MAC protocol for underwater communication network that allows 'peer-to-peer' communication between a surface ship and multiple underwater systems is designed, and the proposed control protocol is implemented for its verification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.11
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• pp.2527-2534
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• 2010

The vehicle consists of a variety of in-vehicle networks and each network uses its own protocol. It makes the communication between the heterogeneous networks and the extension of a new vehicle network difficult. It is also difficult to provide a variety of services between the networks. Therefore, a method for communication and extension between in-vehicle networks is essentially required. In this paper, a XML schema which focuses on the communication and extension of the networks is proposed. It is based on a standard protocol. We also implement the XML, builder and parser tool. The implementation shows that the proposed schema is in the capacities of communication and extension. It also shows that each message from the existing vehicle networks is matched well with the corresponding intelligent service.

• 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 Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1037-1045
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• 1996

In this paper, the parallel model for stereo camera is employed to find the heralding angle and the distance between a leading vehicle and the following vehicle, BART(Binocular Autonomous Research Team vehicle). Two TDNNs (Time Delay Neural Network) such as S-TDNN and A-TDNN are introduced to control BART. S-TDNN controls the speed of the following vehicle while A-TDNN controls the steering angle of BATR. A human drives BART to collect data which are used for training the said neural networks. The trained networks performed the vehicle tracking function satisfactorily under the same driving conditions performed by the human driver. The neural network approach has good portability which decreases costs and saves development time for the different types of vehicles.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.5
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• pp.969-975
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• 2002

Visual effects are important cues for providing occupants with virtual reality in a vehicle simulator which imitates real driving. The viewpoint of an occupant is sensitively dependent upon the occupant's posture, therefore, the total human body motion must be considered in a graphic simulator. A real-time simulation is required for the dynamic analysis of complex human body motion. This study attempts to apply a neural network to the motion analysis in various driving situations. A full car of medium-sized vehicles was selected and modeled, and then analyzed using ADAMS in such driving conditions as bump-pass and lane-change for acquiring the accelerations of chassis of the vehicle model. A hybrid III 50%ile adult male dummy model was selected and modeled in an ellipsoid model. Multibody system analysis software, MADYMO, was used in the motion analysis of an occupant model in the seated position under the acceleration field of the vehicle model. Acceleration data of the head were collected as inputs to the viewpoint movement. Based on these data, a back-propagation neural network was composed to perform the real-time analysis of occupant motions under specified driving conditions and validated output of the composed neural network with MADYMO result in arbitrary driving scenario.