• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.03a
• /
• pp.107-111
• /
• 1998

This paper presents a now approach to the design of intelligent contorl system for track vehicle system using fuzzy logic based on neural network. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. Moreover, We develop a Windows 95 version dynamic simulator which can simulate a track vehicle model in 3D graphics space. It is proposed a learning controller consisting of two neural network-fuzzy based of independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The dynamic simulator for track vehicle is developed by Microsoft Visual C++. Graphic libraries, OpenGL, by Silicon Graphics, Inc. were utilized for 3D Graphics. The performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.

• ETRI Journal
• /
• v.45 no.6
• /
• pp.1065-1078
• /
• 2023

In vehicular networks, diverse safety information can be shared among vehicles through internet connections. In vehicle-to-internet communications, vehicles on the road are wirelessly connected to different cloud networks, thereby accelerating safety information exchange. Onboard sensors acquire traffic-related information, and reliable intermediate nodes and network services, such as navigational facilities, allow to transmit safety information to distant target vehicles and stations. Using vehicle-to-network communications, we minimize delays and achieve high accuracy through consistent connectivity links. Our proposed approach uses intermediate nodes with two-hop separation to forward information. Target vehicle detection and routing of safety information are performed using machine learning algorithms. Compared with existing vehicle-to-internet solutions, our approach provides substantial improvements by reducing latency, packet drop, and overhead.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.8 no.2
• /
• pp.69-78
• /
• 2013

As vehicles become more intelligent for safety and convenience of drivers, in-vehicle networking systems such as controller are network (CAN) have been widely used due to increasing number of electronic control unit (ECU). Recently, FlexRay was developed to replace CAN protocol in chassis networking systems, to remedy the shortage of transmission capacity and unsatisfactory real-time transmission delay of conventional CAN. However, it is difficult for vehicle network designers to calculate platform configuration registers (PCR) and determine a base cycle or slot length of FlexRay. To assist vehicle network designers for designing FlexRay cluster, this paper presents automatic field bus exchange format (FIBEX) generation algorithm from CANdb information, which is de-facto standard database format for CAN. To design this program, structures of FIBEX, CANdb and relationship among PCR variables are analyzed.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.131-134
• /
• 1997

This paper presents a new approach to the design of intelligent control system for track vehicle system using fuzzy logic based on neural network. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1997.10a
• /
• pp.70-75
• /
• 1997

This paper proposes a new approach to the design of fuzzy-neuro control for track vehicle system using fuzzy logic based on neural network. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based of independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1014-1019
• /
• 2005

In this paper, a neural network technique for automatic steering control of a four wheel drive autonomous highway snowplow vehicle is presented. Controllers are designed by the LQR method based on the vehicle model. Then, neural network is used as an auxiliary controller to minimize lateral tracking error under the presence of load. Simulation studies of LQR control and neural network control are conducted for the vehicle model under a virtual snowplowing situation. Tracking performances are also compared for two and four wheeled steering vehicles.

• ETRI Journal
• /
• v.42 no.3
• /
• pp.411-419
• /
• 2020

In real-world intelligent transportation systems, accuracy in vehicle license plate detection and recognition is considered quite critical. Many algorithms have been proposed for still images, but their accuracy on actual videos is not satisfactory. This stems from several problematic conditions in videos, such as vehicle motion blur, variety in viewpoints, outliers, and the lack of publicly available video datasets. In this study, we focus on these challenges and propose a license plate detection and recognition scheme for videos based on a temporal matching prior network. Specifically, to improve the robustness of detection and recognition accuracy in the presence of motion blur and outliers, forward and bidirectional matching priors between consecutive frames are properly combined with layer structures specifically designed for plate detection. We also built our own video dataset for the deep training of the proposed network. During network training, we perform data augmentation based on image rotation to increase robustness regarding the various viewpoints in videos.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.19 no.4
• /
• pp.127-136
• /
• 2023

Many research results on the traffic enforcement of illegal driving of twowheeled motor vehicles using license plate recognition are introduced. Deep learning convolutional neural networks can be used for character and word recognition of license plates because of better generalization capability compared to traditional Backpropagation neural networks. In the plates of twowheeled motor vehicles, the interdependent government and city words are included. If we implement the mutually independent word recognizers using error correction rules for two word recognition results, efficient license plate recognition results can be derived. The CPU based convolutional neural network without library under real time processing has an advantage of low cost real application compared to GPU based convolutional neural network with library. In this paper twowheeled motor vehicle license plate recognition algorithm is introduced using CPU based deep-learning convolutional neural network. The experimental results show that the proposed plate recognizer has 96.2% success rate for outdoor twowheeled motor vehicle images in real time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.10a
• /
• pp.211-215
• /
• 2015

It comes into the spotlight as the new Blue Ocean in which the connected car industry in which the car and mobile communication technology is convergence. All sorts of infortainments services connecting with the portable electronic device(Smart phone, tablet PC, and MP3 player) and car are rapidly grown. The Connected car emphasizes the vehicle connectivity with the concept that the car has communication with the around on a real time basis and it provides the safety and expedience to the operator and using the thing of Internet (IoT) in the car and supports the application, presently, the entertainment service including the real-time Navigation, parking assistant function, not only the remote vehicle control and management service but also Email, multimedia streaming service, SNS and with the platform. Intelligent vehicle network is studied as the kind according to MANET(Mobile Ad Hoc Network) for the safety operation of the cars of the road and improving the efficiency of the driving. The intelligent vehicle network is comprised for the driving information offering changing rapidly of the communication(V2V: Vehicle to Vehicle) between the car and the car, communication(V2I : Vehicle to Infrastructure) between the infrastructure and the car, and V2X (Vehicle to Nomadic).

• Journal of Sensor Science and Technology
• /
• v.10 no.5
• /
• pp.329-336
• /
• 2001

This paper describes the steering control and geomagnetism cancellation for an autonomous vehicle using an MR sensor. The magneto-resistive (MR) sensor obtains the vector summation of the magnetic fields from embedded magnets and the Earth. The vehicle is controlled by the magnetic fields from embedded magnets. So, geomagnetism is the disturbance in the steering control system. In this paper, we propose a new method of the sensor arrangement in order to remove the geomagnetism and vehicle body interference. The proposed method uses two MR sensors located in a level plane and the steering controller has been developed. The controller has three input variables ($dB_x$, $dB_y$, $dB_z$) using the measured magnetic field difference, and an output variable (the steering angle). A simulation program was developed to acquire the data to teach the neural network, in order to test the ability of a neural network to learn the steering control process. Also, the 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. From the simulation and field test, good result was obtained and we confirmed the robustness of the neural network controller in a real autonomous vehicle.