• Journal of the Korean Society of Manufacturing Process Engineers
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• v.12 no.1
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• pp.70-76
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• 2013

Recently, the performance and function of electrical and electronic system in automotive vehicles is developing at a rapid rate with the advancement of IT technologies. Combined together with micro-controller and sensor technologies, the Vehicle Smart System (VSS) being developed to improve driver's convenience and comfort has been employed to a variety of applications. In addition to the convenience system, the Auto-parking Assistance System (AAS) that is now attracting a new attention has been already applied to some vehicles, but it is currently limited to luxury car models only. In this paper, we present a fuzzy controller that enables autonomous parking assistance without the AAS. The controller can perform the assistance with information provided from moving status, current position and steering angle as one is able to park a car based on his/her experience and knowledge for driving and parking. We have evaluated its performance of the proposed controller by simulation and tested the excellence of the controller by building a model vehicle embedded with the micro-controllers.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.3
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• pp.57-64
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• 2020

The leading technologies of the ADAS (Advanced Driver Assist System) are ACC (Advanced Cruise Control), LKAS (Lane Keeping Assist System), and AEB (Autonomous Emergency Braking). LKAS is a system that uses cameras and infrared sensors to control steering and return to its running lane in the event of unintentional deviations. The actual test is performed for a safety evaluation and verification of the system. On the other hand, research on the system evaluation method is insufficient when an additional steering angle is applied. In this study, a model using Prescan was developed and simulated for the scenarios proposed in the preceding study. Comparative analyses of the simulation and the actual test were performed. As a result, the modeling validity was verified. A difference between the front wheels and the lane occurred due to the return velocity. The results revealed a maximum error of 0.56 m. The error occurred because the lateral velocity of the car was relatively small. On the other hand, the distance from wheels to the lanes displayed a tendency of approximately 0.5 m. This can be verified reliably.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.3
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• pp.645-650
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• 2010

In this paper we present the Bimodal-tram simulator using the PXI embedded real-time controllers. The Bimodal-tram is developed in KRRI (Korea Railroad Research Institute). The vehicle can be automatically operated by navigation control system (NCS). For the automatic driving, the vehicle lanes will be marked with permanent magnets that are placed in the ground. The vehicle is controlled by NCS. NCS governs the manual mode and automatic mode driving. The simulator is designed by an identical conception with the real control condition. The dynamic motion of vehicle is simulated by the nonlinear dynamic model. The control computer calculates the control values. The signal interface is linked by CAN communication. The simulation is processed by real-time base. The test driver can see the graphic motion of vehicle and can operate the steering wheel, gas and brake pedal to control direction and velocity of vehicle during the simulation. At present, the simulator is only operated by manual mode. The automatic mode will be linked after the control algorithm is finished. We will use the simulator to develop the control algorithm in the automatic mode. This paper shows the simulator designed for Bimodal-tram using real-time based controller. The results of the test using the simulator are presented and discussed.

• 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.23 no.11 s.170
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• pp.1960-1969
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• 1999

This paper presents dynamic modeling and controller design of a tracked vehicle installed with the double rod type ERSU(electro-rheological suspension unit). A 16 degree-of-freedom model for the tracked vehicle is established by Lagrangian method followed by the formulation of a new sky-ground hook controller. This controller takes account for both the ride quality and the steering stability. The weighting parameter between the two performance requirements is adopted to adjust required performance characteristics with respect to the operation conditions such as road excitation. The parameter is appropriately determined by employing a fuzzy algorithm associated with the vehicle motion. Computer simulations are undertaken in order to demonstrate the effectiveness of the proposed control system. Acceleration values at the driver's seat are analyzed under bump road profile, while frequency responses of vertical acceleration are investigated under random road excitation.

• Journal of Power Electronics
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• v.13 no.4
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• pp.536-545
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• 2013

In this study, an integrated motor control algorithm for an in-wheel electric vehicle is suggested. It consists of slip control that controls the in-wheel motor torque using the road friction coefficient and slip ratio; yaw rate control that controls the in-wheel motor torque according to the road friction coefficient and the yaw rate error; and velocity control that controls the vehicle velocity by a weight factor based on the road friction coefficient and the yaw rate error. A co-simulator was developed, which combined the vehicle performance simulator based on MATLAB/Simulink and the vehicle model of CarSim. Based on the co-simulator, a human-in-the-loop simulation environment was constructed, in which a driver can directly control the steering wheel, the accelerator pedal, and the brake pedal in real time. The performance of the integrated motor control algorithm for the in-wheel electric vehicle was evaluated through human-in-the-loop simulations.

• International Journal of Automotive Technology
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• v.6 no.2
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• pp.141-148
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• 2005

In order to reduce the time and costs of improving the performance of vehicle suspensions, the techniques for optimizing damping and air spring characteristic were proposed. A full vehicle model for a bus is constructed with a car body, front and rear suspension linkages, air springs, dampers, tires, and a steering system. An air spring and a damper are modeled with nonlinear characteristics using experimental data and a curve fitting technique. The objective function for ride quality is WRMS (Weighted RMS) of the power spectral density of the vertical acceleration at the driver's seat, middle seat and rear seat. The objective function for handling performance is the RMS (Root Mean Squares) of the roll angle, roll rate, yaw rate, and lateral acceleration at the center of gravity of a body during a lane change. The design variables are determined by damping coefficients, damping exponents and curve fitting parameters of air spring characteristic curves. The Taguchi method is used in order to investigate sensitivity of design variables. Since ride and handling performances are mutually conflicting characteristics, the validity of the developed optimum design procedure is demonstrated by comparing the trends of ride and handling performance indices with respect to the ratio of weighting factors. The global criterion method is proposed to obtain the solution of multi-objective optimization problem.