• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1089-1099
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• 2015

This paper proposes vision-based collision risk estimation method for an unmanned surface vehicle. A robust image-processing algorithm is suggested to detect target obstacles from the vision sensor. Vision-based Target Motion Analysis (TMA) was performed to transform visual information to target motion information. In vision-based TMA, a camera model and optical flow are adopted. Collision risk was calculated by using a fuzzy estimator that uses target motion information and vision information as input variables. To validate the suggested collision risk estimation method, an unmanned surface vehicle experiment was performed.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.9
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• pp.212-219
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• 1999

This paper describes the NANDO VDC (Vehicle Dynamics Control) system for the vehicle stability enhancement and consists of the control strategies , computer simulation and tests on the various road surface. This VDC system controls the dynamic vehicle motion in the emergency situation such as the final oversteer/understeer andallows the vehicle to follow the course as desired by the driver. The system is based on an active yaw control and its performance verified by the test is shown. Also the comparison between the MANDO VDC System and a competitor is carried out.

• Transactions of the Korean Society of Automotive Engineers
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• v.17 no.2
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• pp.1-9
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• 2009

A parking-assist system is defined that a driver adjusts vehicle velocity through brake pedal operation and parking-assist system controls the motion of the vehicle to follow a collision-free path. In this study, a motion control algorithm using Fuzzy inference is proposed to track a maneuvering clothoid parallel path. Simulations are performed under SIMULINK environments using MATLAB and CarSim for a vehicle model. As the vehicle model in MATLAB a bicycle model is used including lateral dynamics. The simulation results show that the path tracking performance is satisfactory under various driving and initial conditions.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.888-891
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• 1991

In this paper, we have shown a new approach of neural networks for mobile robot motion control under an indoor refracted environment. The vehicle has two powered wheels and four passive casters which support a free motion. And it also uses sonar sensors, infrared sensors, Internal odometer, and contact sensors. Two experiments were conducted to demonstrate our objectives. The first one is that the vehicle executes a reflexive motor control to maintain a constant distance to the boundary. The second one is that as well as the boundary following, the vehicle makes a block obstacle avoidance during its path. Without prior knowledge of external environment. we have accomplished the tasks by employing a simple, reactive stimulus-response neural network scheme associating sensor data with the vehicle's action.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.6
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• pp.513-520
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• 2011

This paper presents the motion planning of robotic vehicles for the path tracking and the obstacle avoidance. To follow the given path, the vehicle moves through the turning radius obtained through the pure pursuit method, which is a geometric path tracking method. In this paper, we assume that the vehicle is equipped with a 2D laser scanner, allowing it to avoid obstacles within its sensing range. The turning radius for avoiding the obstacle, which is inversely proportional to the virtual force, is then calculated. Therefore, these two kinds of the turning radius are used to generate the steering angle for the front wheel of the vehicle. And the vehicle reduces the velocity when it meets the obstacle or the large steering angle using the potentials of obstacle points and the steering angle. Thus the motion planning of the vehicle is done by planning the steering angle for the front wheels and the velocity. Finally, the performance of the proposed method is tested through simulation.

• International Journal of Automotive Technology
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• v.8 no.4
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• pp.445-453
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• 2007

This paper presents a robust controller design approach for improving vehicle dynamic roll motion performance and guaranteeing the closed-loop system stability in spite of vehicle parameter variations resulting from aging elements, loading patterns, and driving conditions, etc. The designed controller is linear parameter-varying (LPV) in terms of the time-varying parameters; its control objective is to minimise the $H_{\infty}$ performance from the steering input to the roll angle while satisfying the closed-loop pole placement constraint such that the optimal dynamic roll motion performance is achieved and robust stability is guaranteed. The sufficient conditions for designing such a controller are given as a finite number of linear matrix inequalities (LMIs). Numerical simulation using the three-degree-of-freedom (3-DOF) yaw-roll vehicle model is presented. It shows that the designed controller can effectively improve the vehicle dynamic roll angle response during J-turn or fishhook maneuver when the vehicle's forward velocity and the roll stiffness are varied significantly.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.531-533
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• 1999

The main aim of this paper is to investigate the possibility of applying fuzzy control algorithms to a microprocessor-based servomotor controller which requires faster and more accurate response compared with many other industrial processes. In addition, this study deals with the control of the lateral motion of a mobile vehicle. A adaptive fuzzy logic controller(AFLC) is designed and applied to a experimental mobile vehicle in order to achieve control of the lateral motion of the vehicle.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.451-459
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• 2011

Fundamentally, there are 5 systems are needed for autonomous navigation of unmanned ground vehicle: Localization, environment perception, path planning, motion planning and vehicle control. Path planning and motion planning are accomplished based on result of the environment perception process. Thus, high reliability of localization and the environment perception will be a criterion that makes a judgment overall autonomous navigation. In this paper, via map matching using vehicle dynamic model and LIDAR sensors, replace high price localization system to new one, and have researched an algorithm that lead to robust autonomous navigation. Finally, all results are verified via actual unmanned ground vehicle tests.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.8
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• pp.823-830
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• 2009

For developing automotive parts and telematics devices the real car test often shows limitation because it needs high cost, much time and has the possibility of the accident. Therefore, a Vehicle Driving Simulator (VDS) instead of the real-car test has been used by some automotive manufactures, research centers, and universities. The VDS is a virtual reality device which makes a human being feel as if one drives a vehicle actually. Unlike actual vehicle, the simulator has limited kinematic workspace and bounded dynamic characteristics. So it is difficult to simulate dynamic motions of a multi-body vehicle model fully. In order to overcome these problems, a washout algorithm which restricts workspace of the simulator within the kinematic limits is needed, and analysis of dynamic characteristics is required also. However, a classical washout algorithm contains several problems such as time delay and generation of wrong motion signal caused by characteristics of filters. Specially, the classical washout algorithm has the simulator sickness when driver hardly turns brakes and accelerates the VDS. In this paper, a new washout algorithm is developed to enhance the motion sensitivity and improve the simulator sickness by using the vehicle tilt signal which is generated in the real time vehicle dynamic model.

• Journal of Auto-vehicle Safety Association
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• v.15 no.4
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• pp.16-22
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• 2023

This paper presents a proactive longitudinal motion planning algorithm for improving the safety of an automated bus. Since the field of view (FOV) of the autonomous vehicle was limited depending on onboard sensors' performance and surrounding environments, it was necessary to implement vehicle-to-vehicle (V2V) communication for overcoming the limitation. After a virtual V2V-equipped target was constructed considering information obtained from V2V communication, the reference motion of the ego vehicle was determined by considering the state of both the V2V-equipped target and the sensor-detected target. Model predictive control (MPC) was implemented to calculate the optimal motion considering the reference motion and the chance constraint, which was deduced from manual driving data. The improvement in driving safety was confirmed through vehicle tests along actual urban roads.