• Structural Engineering and Mechanics
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• v.51 no.2
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• pp.315-331
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• 2014

The present study investigated the effect of the correlation of the measured road roughness profiles corresponding to the left and right wheels of a vehicle on the vibration of a vehicle-bridge coupling system. Four sets of road roughness profiles were measured by a laser road-testing vehicle. A correlation analysis was carried out on the four roughness samples, and two samples with the strongest correlation and weakest correlation were selected for the power spectral density, autocorrelation and cross-correlation analyses. The scenario of a three-axle truck moving across a rigid-frame arch bridge was used as an example. The two selected road roughness profiles were used as inputs to the vehicle-bridge coupling system. Three different input modes were adopted in the numerical analysis: (1) using the measured road roughness profile of the left wheel for the input of both wheels in the numerical simulation; (2) using the measured road roughness profile of the right wheel for both wheels; and (3) using the measured road roughness profiles corresponding to left and right wheels for the input corresponding to the vehicle's left and right wheels, respectively. The influence of the three input modes on the vibration of the vehicle-bridge system was analyzed and compared in detail. The results show that the correlation of the road roughness profiles corresponding to left and right wheels and the selected roughness input mode both have a significant influence on the vibration of the vehicle-bridge coupling system.

• Journal of Environmental Impact Assessment
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• v.23 no.2
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• pp.112-118
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• 2014

The application of a 3-d noise prediction model is increasing as a tool for performing actual noise assessment in order to investigate the noise impact of the residential facility around a development region. However, because the appropriate plans of applying a 3-d noise prediction model is insufficient, it is important to secure the reliability of the noise prediction results generated by a 3-d noise prediction model. Therefore, this study is focused on examining a 3-d noise prediction model, and a prediction equation and input data in it. For this, the 3-d noise prediction models such as SoundPLAN, Cadna-A, IMMI is applied in road noise. After the contents of road noise equations, input data of road noise source, and input data of road noise barrier are understood, the road noise prediction results are compared and examined according to the variation of 3-d noise prediction model, road noise equation, and input data of road noise source and road noise barrier.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.5
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• pp.550-557
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• 1999

This paper describes a road-following controller using the proposed neural network for autonomous vehicle. Road-following with visual sensor like camera requires intelligent control algorithm because analysis of relation from road image to steering control is complex. The proposed neural network, relative similarity modular network(RSMN), is composed of some learning networks and a partitioniing network. The partitioning network divides input space into multiple sections by similarity of input data. Because divided section has simlar input patterns, RSMN can learn nonlinear relation such as road-following with visual control easily. Visual control uses two criteria on road image from camera; one is position of vanishing point of road, the other is slope of vanishing line of road. The controller using neural network has input of two criteria and output of steering angle. To confirm performance of the proposed neural network controller, a software is developed to simulate vehicle dynamics, camera image generation, visual control, and road-following. Also, prototype autonomous electric vehicle is developed, and usefulness of the controller is verified by physical driving test.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.2
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• pp.102-109
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• 2000

This paper deals with an observer-based preview control for semi-active suspensions. An Observer has been designed such that all the state variables and road inputs can be estimated from accelerations. Since the road input to the rear wheels is a delayed version of that to the front wheels, it can be obtained by estimating the road input to the front wheels without preview the estimated values of state and the estimated road input has been investigated. The results show that the observer-based control can provide good performance. The observer-based preview control improves the dynamic behavior of the rear axle and that of pitch motion compared to the LQ optimal control.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.779-784
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• 2008

This paper presents a the method for estimating the noise source contribution on the road noise of the vehicle in a multiple input system where the input sources may be coherent with each other. By coherence function method, it is found that the biggest part of the noise source in the road noise is generated by structural vibration on the mechanical-acoustic transfer functions of vehicles. This analysis is modeled as four input/single output system because the noise is generated with four wheels that mechanism of the road noise is very complicated. The coherence function method is proved to be useful tool for identifying of noise source. The overall levels of the interior noise be coherence function method are compared with those measured and calculated by the frequency response function approach using mechanical excitation test. The experimental results have shown a good agreement with the results calculated by the coherence function method when the input sources are coherent strongly each other. The estimation of the road noise indicates that significant coherent can be achieved in the vehicle interior noise.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.11
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• pp.1206-1212
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• 2008

This paper presents a the method for estimating the noise source contribution on the road noise of the vehicle in a multiple input system where the input sources may be coherent with each other. By coherence function method, it is found that the biggest part of the noise source in the road noise is generated by structural vibration on the mechanical-acoustic transfer functions of vehicles. This analysis is modeled as four input/single output system because the noise is generated with four wheels that mechanism of the road noise is very complicated. The coherence function method is proved to be useful tool for identifying of noise source. The overall levels of the interior noise be coherence function method are compared with those measured and calculated by the frequency response function approach using mechanical excitation test. The experimental results have shown a good agreement with the results calculated by the coherence function method when the input sources are coherent strongly each other. The estimation of the road noise indicates that significant coherent can be achieved in the vehicle interior noise.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.8 s.113
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• pp.806-813
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• 2006

An important factor of vibration test using MAST(multi axial simulation table) system is the reliance of input excitation source. Generally the generation of input excitation source is obtained by the measured data on special road in proving ground. The measured data on special road have more exciting energy than the data of real fields, therefore the time and expense for test can be reduced. But the magnitude of input excitation source must be defined by comparison with the excited energy on real field. The object of this paper makes the data base of domestic roads for the definition of input excitation source which is obtained by the measured data on special road in proving ground. These real field data on domestic roads are analyzed by the power spectral density and interrelationship index.

• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.187-192
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• 2017

Autonomous driving vehicle research demands complex road and lane understanding such as lane departure warning, adaptive cruise control, lane keeping and centering, lane change and turn assist, and driving under complex road conditions. A fast and robust road lane detection subsystem is a basic but important building block for this type of research. In this paper, we propose a method that performs road lane detection from black box input. The proposed system applies Random Sample Consensus to find the best model of road lanes passing through divided regions of the input image under HSV color model. HSV color model is chosen since it explicitly separates chromaticity and luminosity and the narrower hue distribution greatly assists in later segmentation of the frames by limiting color saturation. The implemented method was successful in lane detection on real world on-board testing, exhibiting 86.21% accuracy with 4.3% standard deviation in real time.

• Transactions of the Korean Society of Automotive Engineers
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• v.5 no.4
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• pp.84-92
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• 1997

Among the various sources of vehicle interior noise, this paper concerns the road induced noise ; the identification of its transfer path by using experimental method. Multiple input and single output model is taken as a noise generation model. Because it is impossible to measure the road imput forces directly, the acceleration signals are measured on four axle;three directions for each point. By considering the cross correlations of input signals, four uncorrelated source groups are taken. Multiple coherence function is employed to investigate the contribution of each group. In addtion, to identify the detailed path through the suspension systems, the contributions of all possible paths are ranked by using the coherence functions between interior noise and the relative accelerations of connections such as bushings and mountings. Measurements are performed with passenger vehicle traveling on concrete and asphalt roads at 60㎞/h.

• International Journal of Automotive Technology
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• v.7 no.5
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• pp.547-554
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• 2006

This paper is concerned with an optimal control suspension system using the preview information of road input based on a quarter car model. The main purpose of the control is to combine good vibration isolation characteristics with improved attitude control. The optimal control law is derived with the use of calculus of variation, consisting of three parts. The first part is a full state feedback term that includes integral control acting on the suspension deflection to ensure zero steady-state deflection in response to static body forces and ramp road inputs. The second part is a feed-forward term which compensates for the body forces when they can be detected, and the third part depends on previewed road input. The performance of the suspension is evaluated in terms of frequency domain characteristics and time responses to ramp road input and cornering forces. The effects of each part of the suspension controller on the system behavior are examined.