• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1481-1484
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• 1997

The vehicle driving simulator expects vehicle motion with real-time simulation arise from driver's steering, accelerating, stopping and simulates motion of vehicl with visula, audio and washout algorithm. And it gives a vivid feeling to driver in reality. Vehicle driving simulator with vehicle integration control system is used for analysis of analysis of vehicle controllaility, steering capacity and safety in various pseudo environment alike. basides, it analyzeds vehicle safety factor dirver's reaction and promotes traffic safety without driver's own risks. The main proceduress of development of the vehicle driving simulator are classified by 3 parts. first the motion base system which can be generated by the motion queues, should be developed. Secondly, real-time vehicle software which can afford the vehicle dynamics, might be constructed. The third procedure is the integration of vehicle driing simulator which can be interconnected between visual systems with motion base. In this study, we are to study of the motion base for a vehicle driving simulator design and that of its real time control and using an extra gyro sensor and accelerometers to find a position and an orientatiion of the moving platform except for calculating forward kinematics. To drive the motion base, we use National Instruments corp's Labview software. Furthemore, we use analysis module for the vehicle motionand the washout algorithm module to consummate driving simulator, which can be driven by human in reality, so we are doing experimentally process about various vehicle motion conditon.

• Journal of the Korean Society of Safety
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• v.14 no.4
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• pp.85-92
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• 1999

Vehicle rollover is an important issue for the traffic safety. Rollover can occur from the driver's action, the vehicle characteristics, or the road condition. This study is about the rollover propensity analysis of a jeep vehicle using the steering and braking maneuver, which is the combined result by the driver and the vehicle. Simple equations of roll motion is used to analyze the roll motion and a special purpose vehicle dynamics program is used to simulate the rollover of the jeep vehicle. From the simulation, an incipient rollover motion of the vehicle was found. However, the more complete rollover propensity analysis would require further investigation using roll dynamic sensitivity study.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.6
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• pp.52-63
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• 1995

This paper presents a detailed derivation of the equations of motion for the stable member of a four-axis platform. Gimbal bearing friction is considered for motion analysis. First, dynamic characteristics of platform, gimbal and gyro with Coulomb friction are studied due to vehicle's angular motion. Second, Vehicle's motion is assumed the sinusoidal function and dynamic characteristics of platform, gimbal and gyro are studied. Conclusively, considering effects of Coulomb friction, they could not follow the vehicle's angular motion and have constant errors. In case of sinusoidal motion, relative angles for each gimbal are amplified, but they are sinusoidal function with almost the same phases.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.4
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• pp.548-555
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• 2011

This paper presents a new method of measuring the ground's gradient using LNS(land navigation system) navigation data. When a vehicle equipped with LNS arrives at any place, LNS provides its navigation data which contain the information on vehicle's motion. We developed some formulas which can explain correlation between the vehicle's motion and ground's gradient. The proposed method using those formulas is shown to be accurate and convenient.

• Journal of the Ergonomics Society of Korea
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• v.32 no.3
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• pp.217-227
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• 2013

Objective: The purpose of this study was to analyze 'response time', 'peak response time' and 'overshoot value' for each muscle by applying the EMG signal to the vehicle response in ISO 7401 and to quantify the response of the driver according to vehicle characteristics by comparing vehicle characteristics and muscle responses of the driver. Background: The Open-loop test defined in international standards ISO 7401 is the only method for evaluating the performance of the vehicle. However, this test was focused only on mechanical responses, not driver's ones. Method: One skilled male driver(22 yrs. experience) was participated in this experiment to measure muscle activities of the driver in transient state. Then the seven muscle signals were applied to calculate 'response time', 'peak response time', and 'overshoot value'. Results: In the analyses of the EMG data, the effects of vehicle type and muscle were statistically significant on the 'response time' and 'peak response time'. Also, the effects of vehicle type, muscle, and lateral acceleration level were statistically significant on the 'overshoot value' in this study. According to the analyses of the vehicle motion data, vehicle motion variable(LatAcc, Roll, YawVel) was statistically significant on the 'response time' and vehicle type, vehicle motion variable, and lateral acceleration level were statistically significant on the 'peak response time', respectively. Conclusion: In the analyses of the 'response time' and 'overshoot value', the data of muscle activities(EMGs) was better index that could evaluate the vehicle characteristic and performance than the data of vehicle motion. In case of peak response time, both EMG and vehicle motion data were good index. Application: The EMGs data from a driver might be applicable as index for evaluation of various vehicle performances based on this study.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.13 no.2
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• pp.165-178
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• 2000

The simplified model for 3-dimensional analysis of vehicle-bridge interactions is presented in this study. By using the analysis model which includes the eccentricity of axle loads and the effect of the torsional forces acting on the bridge, the more accurate analysis results of the behavior of the bridge can be obtained. The equations of kinetic energy, potential energy and damping energy are expressed by degrees of freedom of the vehicle and the bridge. And then by applying Lagrange's equations of motion, the equations of motion of the vehicle and the bridge are obtained. By deriving the equations of forces acting on the bridge considering the vehicle-bridge vertical interactions and also by identifying the position of vehicle as time goes by, mass matrix, stiffness matrix, damping matrix and load vector of vehicle-bridge system are constructed in accordance with the position of vehicles. Then using Newmark's β-method(average acceleration), the equations of motion for the total vehicle bridge system are solved.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.428-435
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• 2017

In this paper, we propose a novel motion field estimation algorithm for which a U-disparity map and forward-and-backward error removal are applied in a vehicular environment. Generally, a motion exists in an image obtained by a camera attached to a vehicle by vehicle movement; however, the obtained motion vector is inaccurate because of the surrounding environmental factors such as the illumination changes and vehicles shaking. It is, therefore, difficult to extract an accurate motion vector, especially on the road surface, due to the similarity of the adjacent-pixel values; therefore, the proposed algorithm first removes the road surface region in the obtained image by using a U-disparity map, and uses then the optical flow that represents the motion vector of the object in the remaining part of the image. The algorithm also uses a forward-backward error-removal technique to improve the motion-vector accuracy and a vehicle's movement is predicted through the application of the RANSAC (RANdom SAmple Consensus) to the previously obtained motion vectors, resulting in the generation of a motion field. Through experiment results, we show that the performance of the proposed algorithm is superior to that of an existing algorithm.

• Journal of Auto-vehicle Safety Association
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• v.11 no.3
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• pp.30-36
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• 2019

This paper suggests a car-following algorithm for urban environment, with multiple target candidates. Until now, advanced driver assistant systems (ADASs) and self-driving technologies have been researched to cope with diverse possible scenarios. Among them, car-following driving has been formed the groundwork of autonomous vehicle for its integrity and flexibility to other modes such as smart cruise system (SCC) and platooning. Although the field has a rich history, most researches has been focused on the shape of target trajectory, such as the order of interpolated polynomial, in simple single-lane situation. However, to introduce the car-following mode in urban environment, realistic situation should be reflected: multi-lane road, target's unstable driving tendency, obstacles. Therefore, the suggested car-following system includes both in-lane preceding vehicle and other factors such as side-lane targets. The algorithm is comprised of three parts: path candidate generation and optimal trajectory selection. In the first part, initial guesses of desired paths are calculated as polynomial function connecting host vehicle's state and vicinal vehicle's predicted future states. In the second part, final target trajectory is selected using quadratic cost function reflecting safeness, control input efficiency, and initial objective such as velocity. Finally, adjusted path and control input are calculated using model predictive control (MPC). The suggested algorithm's performance is verified using off-line simulation using Matlab; the results shows reasonable car-following motion planning.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.1147-1156
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• 1993

In order to analyze lateral control in the backward maneuver of a tractor -trailer combination , a kinematic vehicle model and a human operator model with time delay were utilized for the operator/vehicle system. The analysis was carried out using the frequency domain approach. The open-loop stability of the vehicle motion was analyzed through the transfer functions. The sensitivity of the stability of the vehicle motion. to a change in the steering angle, was also analyzed. A mathematical model of the closed -loop operator/vehicle system was then formulated. The closed -loop stability of the operator /vehicle system was then analyzed. The effect of the delay time on the system was also analyzed through computer simulation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.5
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• pp.95-107
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• 2016

The axle weight of a vehicle in motion can be measured with a low-speed or high-speed weigh-in-motion (WIM). However, the axial load dynamically change depending on the vehicle's characteristics-such as the chassis or axle structure-or the characteristics of the driving environment such as road flatness. The changes in dynamic load lead to differences between the vehicle's weight measured at rest and the vehicle's weight measured in motion. For this Study, an experiment was conducted with an instrumented vehicle to analyze the range of errors caused by uncontrollable environmental factors by identifying the characteristics of the dynamic load changes of a vehicle in motion, and determine the appropriate scale for the accuracy evaluation of a high-speed WIM, as a preparatory research for the introduction of unmanned overweight enforcement systems in the future. The key findings from the experiment are summarized as follows. First, The gross weight of the tested vehicle changed by approximately 1% at low velocities and approximately by 4% at high velocities, and the vehicle's axle weight changed by approximately 1-3%, at low velocities and by 2-9% at high velocities. A single axle showed larger weight changes than individual axles in a group. Secondly, The vehicle's gross weight and the axle weight on the impact section were up to eight times and three-to-twelve times higher, respectively, than its gross weight and the axle weight on the flat section. The vibration frequency of the vehicle's dynamic load was measured at between 2.4 and 5.8Hz, and found to return to the normal amplitude after moving approximately 30 meters.