Methods of Assessment of Accuracy of Road Surface Roughness Measurement with Profilometer

Lushnikov, N. A.; Lushnikov, Petr

doi:10.1016/j.trpro.2017.01.069

Cited by 13 publications

(5 citation statements)

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“…Based on the road displacement PSD G q (f) given by Equation (8), the mean square value of unsprung mass acceleration can be described by…”

Section: Analysis Of the Relationship Between Unsprung Mass Acceleratmentioning

confidence: 99%

“…Existing methods for estimating road roughness can be divided into three categories [6]: direct measurements [7][8][9][10][11], noncontact measurements [12][13][14][15], and system response-based estimation [16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…Researchers who tried to measure and evaluate road roughness with direct measurements usually need either a special vehicle or sensors, which makes the method poor in portability. Recently, Japanese researchers Nueraihemaitijiang Abulizi et al had proposed a road roughness estimator based on compact road profiler and ArcGIS [7], which services for the road management instead of systems on the vehicle; Russians researchers Lushnikov and Lushnikov used a profilometer that can measure the longitudinal microprofile of the road surface to assess the road surface roughness coefficients accurately [8]; Balaram and Mostert presented the results of road roughness measurements on a gravel road in KwaZulu-Natal collected with a Roughometer III [9], while Kumar et al chose to use mobile laser scanning data to estimate the road roughness [10]. All the methods above need special sensors attached on the vehicles, but Zhang et al developed a real-time roughness evaluation approach based on the durable in-pavement strain sensors that build in pavement panels [11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Online Classification of Road Roughness Conditions with Vehicle Unsprung Mass Acceleration by Sliding Time Window

Cui

2018

Shock and Vibration

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Suspension control systems are in need for more information of road roughness conditions to improve their performance under different roads. Existing methods of gauging road roughness are limited, and they usually involve visual inspections or special vehicles equipped with instruments that can gauge physical measurements of road irregularities. This paper proposes data collection for a period of a time from accelerometers fixed on unsprung mass and uses the mean square values of this datasets divided by vehicle speed to classify the roughness conditions of a section of a road. This approach is possible due to the existence of relationships between the power spectral densities of the road surface, unsprung mass accelerations via a transfer function, and vehicle speed. This paper gave the relationship between the resolution of road roughness classification and the length of time-window and suggestions about choosing the appropriate time-window length on the balance of road roughness resolution and classification delay. Moreover, to enhance the stability of classification, the influence of damping parameters of vehicle suspension on the classification output is studied, and a classification method of road roughness is proposed based on neural network and damping coefficient correction.

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“…Based on the road displacement PSD G q (f) given by Equation (8), the mean square value of unsprung mass acceleration can be described by…”

Section: Analysis Of the Relationship Between Unsprung Mass Acceleratmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Online Classification of Road Roughness Conditions with Vehicle Unsprung Mass Acceleration by Sliding Time Window

Cui

2018

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…The traditional manual detection method requires manual measurement tools, which have low efficiency and are greatly affected by human factors, so cannot meet the requirements of traffic control. Special equipment, such as laser sensors or high-speed profilers, also needs to be installed on trucks or trailers [ 7 ] rather than conventional passenger vehicles. Although the new compact road profiler [ 8 ] can easily collect profile data, evaluate urban public roads, and monitor road roughness conditions in real time, its cost and size are not suitable for use in conventional vehicles.…”

Section: Introductionmentioning

confidence: 99%

Study on Multi-Mode Switching Control Strategy of Active Suspension Based on Road Estimation

Liu

et al. 2023

Sensors

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In this paper, the least squares method is used to determine the vertical height of the road space domain. Based on the road estimation method, the active suspension control mode switching model is constructed, and the dynamic characteristics of the vehicle in comfort, safety, and integrated modes are analyzed. The vibration signal is collected by the sensor, and the parameters such as vehicle driving conditions are solved for in reverse. A control strategy for multiple mode switching under different road surfaces and speeds is constructed. At the same time, the particle swarm optimization algorithm (PSO) is used to optimize the weight coefficients of LQR control under different modes, and the dynamic performance of vehicle driving is comprehensively analyzed. The test and simulation results show that the road estimation results under different speeds in the same road section are very close to the results obtained by the detection ruler method, and the overall error is less than 2%. Compared with the active suspension controlled by passive and traditional LQR, the multi-mode switching strategy can achieve a better balance between driving comfort and handling safety and stability, and also improve the driving experience more intelligently and comprehensively.

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“…Papadimitrakis et al [14] proposed a road preview model predictive controller that introduced radial signals, used LiDAR to predict the road ahead, and proposed a predictive control scheme for the whole vehicle active suspension system. The accuracy of measurement is high; however, the cost of the system sensors is relatively high, and the accuracy is susceptible to the influence of the external environment, such as the weather [15]. In recent years, scholars have conducted research of the measurement of road roughness based on the suspension response, in which the suspension response is obtained by means of onboard sensors, such as acceleration sensors or dynamic displacement sensors.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy PID Control Strategy for Vehicle Active Suspension Based on Road Evaluation

et al. 2022

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The paper proposes a fuzzy proportion integral differential (PID) control strategy based on road estimation to meet the different performance requirements of active suspension for comfort and safety under different road conditions in view of the uncertainty of vehicle suspension parameters and random road perturbations. First, flexible neural tree (FNT) is used to estimate the vertical disturbance of road within uncertain vehicle parameters in which the Fourier transform of the autocorrelation function is used to fit the road power spectrum density (PSD). In order to compensate for the delay and realize the real-time estimate of road conditions, an online fuzzy evaluation strategy for road condition is developed by integrating the fitted road PSD and the real-time suspension performance. After that, an adaptive fuzzy PID control strategy is developed based on the adaptive suspension control performance, which is supposed to realize the adaptive adjustment of control parameters for different road conditions. The simulation experimental results show that the online fuzzy evaluation strategy can effectively reflect the change in road condition, and the proposed adaptive fuzzy PID control strategy can work for adjusting the parameters adaptively, according to the changes in road conditions and therefore meet the control performance requirements under different road conditions.

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Methods of Assessment of Accuracy of Road Surface Roughness Measurement with Profilometer

Cited by 13 publications

References 0 publications

Online Classification of Road Roughness Conditions with Vehicle Unsprung Mass Acceleration by Sliding Time Window

Online Classification of Road Roughness Conditions with Vehicle Unsprung Mass Acceleration by Sliding Time Window

Study on Multi-Mode Switching Control Strategy of Active Suspension Based on Road Estimation

Adaptive Fuzzy PID Control Strategy for Vehicle Active Suspension Based on Road Evaluation

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