scholarly journals Assessment of the Road Surface Condition with Longitudinal Acceleration Signal of the Car Body

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 5987
Author(s):  
Krzysztof Prażnowski ◽  
Jarosław Mamala ◽  
Michał Śmieja ◽  
Mariusz Kupina
Keyword(s):  
Surface Condition ◽  
Mean Amplitude ◽  
Road Surface ◽  
Vehicle Body ◽  
Frequency Bands ◽  
Body Acceleration ◽  
The Road ◽  
Road Roughness ◽  
Specific Frequency ◽  
The Mean

On the basis of road tests, the authors assessed the feasibility of the vehicle body acceleration values for the purposes of assessing road surface characteristics in terms of its roughness. Short-term Fourier Transform (STFT) was used for the analysis of the recorded signal. The spectra obtained in successive frequency bands demonstrate the amplitudes originating from the natural vibrations of the rolling wheel and forces resulting from the interaction with the road roughness. The article focuses on the relationships between the road roughness and the ratios of individual amplitudes in a specific frequency band of the vehicle body acceleration values. Amplitude values derived on the basis of successive windows were averaged for analogous, arbitrarily assumed local frequency bands. The value characterizing the road surface condition provided the information regarding the mean amplitude value in specific frequency ranges depending on the instantaneous velocity of the car body and the condition of the road surface on which it was moving. In cases where the road was free of any visible roughness, the obtained mean amplitude value in the analyzed spectrum window, for the adopted vehicle velocity range from 50 km h to 100 km/h, did not exceed 0.02 m/s2. It was also demonstrated that the road surface roughness leads to an increase in the mean amplitude value from 0.07 m/s2 to 0.16 m/s2.

scholarly journals Pavement State Recognition Method Considering Slope

2021 ◽  
Vol 2113 (1) ◽  
pp. 012080
Author(s):  
Xiuhao Xi ◽  
Jun Xiao ◽  
Qiang Zhang ◽  
Yanchao Wang
Keyword(s):  
Surface Condition ◽  
Lateral Force ◽  
Longitudinal Force ◽  
Operating Conditions ◽  
Road Surface ◽  
Tire Model ◽  
State Recognition ◽  
Adhesion Coefficient ◽  
The Road ◽  
Real Time Tracking

Abstract For the problem of road surface condition recognition, this paper proposes a real-time tracking method to estimate road surface slope and adhesion coefficient. Based on the fusion of dynamics and kinematics, the current road slope of the vehicle which correct vertical load is estimated. The effect of the noise from dynamic and kinematic methods on the estimation results is removed by designing a filter. The normalized longitudinal force and lateral force are calculated by Dugoff tire model, and the Jacobian matrix of the vector function of the process equation is obtained by combining the relevant theory of EKF algorithm. The road adhesion coefficient is estimated finally. The effectiveness of the algorithm is demonstrated by analyzing the results under different operating conditions, such as docking road and bisectional road, using a joint simulation of Matlab/Simulink and Carsim.

scholarly journals Road Surface Condition Forecasting in France

2009 ◽  
Vol 48 (12) ◽  
pp. 2513-2527 ◽  
Author(s):  
L. Bouilloud ◽  
E. Martin ◽  
F. Habets ◽  
A. Boone ◽  
P. Le Moigne ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Initial Conditions ◽  
Meteorological Data ◽  
Surface Condition ◽  
Road Surface ◽  
Snow Layer ◽  
Surface Conditions ◽  
The Road ◽  
Road Surface Temperature ◽  
On The Road

Abstract A numerical model designed to simulate the evolution of a snow layer on a road surface was forced by meteorological forecasts so as to assess its potential for use within an operational suite for road management in winter. The suite is intended for use throughout France, even in areas where no observations of surface conditions are available. It relies on short-term meteorological forecasts and long-term simulations of surface conditions using spatialized meteorological data to provide the initial conditions. The prediction of road surface conditions (road surface temperature and presence of snow on the road) was tested at an experimental site using data from a comprehensive experimental field campaign. The results were satisfactory, with detection of the majority of snow and negative road surface temperature events. The model was then extended to all of France with an 8-km grid resolution, using forcing data from a real-time meteorological analysis system. Many events with snow on the roads were simulated for the 2004/05 winter. Results for road surface temperature were checked against road station data from several highways, and results for the presence of snow on the road were checked against measurements from the Météo-France weather station network.

scholarly journals Mixed Skyhook and FxLMS Control of a Half-Car Model with Magnetorheological Dampers

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Piotr Krauze ◽  
Jerzy Kasprzyk
Keyword(s):  
Control Strategies ◽  
Mr Damper ◽  
Front Axle ◽  
Vehicle Body ◽  
Magnetorheological Dampers ◽  
The Road ◽  
Quarter Car Model ◽  
Car Model ◽  
Road Roughness ◽  
Suspension Model

The problem of vibration attenuation in a semiactive vehicle suspension is considered. The proposed solution is based on usage of the information about the road roughness coming from the sensor installed on the front axle of the vehicle. It does not need any preview sensor to measure the road roughness as other preview control strategies do. Here, the well-known Skyhook algorithm is used for control of the front magnetorheological (MR) damper. This algorithm is tuned to a quarter-car model of the front part of the vehicle. The rear MR damper is controlled by the FxLMS (Filtered-x LMS) taking advantage of the information about the motion of the front vehicle axle. The goal of this algorithm is to minimize pitch of the vehicle body. The strategy is applied for a four-degree-of-freedom (4-DOF) vehicle model equipped with magnetorheological dampers which were described using the Bouc-Wen model. The suspension model was subjected to the road-induced excitation in the form of a series of bumps within the frequency range 1.0–10 Hz. Different solutions are compared based on the transmissibility function and simulation results show the usefulness of the proposed solution.

A Neural Fuzzy Approach for Controlling Active Vehicle Suspension Systems

2003 ◽  
Author(s):  
M. B. A. Abdelhady ◽  
S. A. Alhasan
Keyword(s):  
Suspension System ◽  
Road Surface ◽  
Control Law ◽  
Error Signal ◽  
Performance Parameters ◽  
Active System ◽  
Control Laws ◽  
Body Acceleration ◽  
The Road ◽  
Neural Fuzzy

In this work, a novel neural fuzzy (NF) control scheme is introduced to design a fully active suspension system. The fuzzy part of the controller handles uncertainties, whereas the neural part learns from past events and tunes the controller to optimize the performance of the suspension system. The two-degree-of-freedom quarter-car model is used to illustrate the control strategy and to evaluate the performance parameters for sinusoidal and random road inputs. A sinusoid road surface description is first used to obtain an initial design of the NF controller. The acceleration of the sprung mass is compared with that of an ideal skyhook model to produce an error signal, e(t); this error signal, as well as Δe(t) are employed as inputs to the controller. Results obtained for this type of road input indicate that the NF active system has significant advantages over the linear quadratic regulator (LQR) active suspension system. In order to get a broader view, more realistic road descriptions and practical control laws were used. The performance parameters were computed when the road surface was presented as a random road input. The control law of the NF active system was modified to achieve a novel non-linear control (NC) strategy. This control law requires only measurement of the body acceleration and the road input displacement, and hence, it can be realized easily in practice when compared with all other control laws, including the LQR one. For a wide range of road surfaces, results show that the performance capability of this novel system is much better than that of the LQR active system. For example, the improvements, under a medium-quality road surface and a 30-m/s vehicle speed, achieved a reduction in the rms values of the ISO weighted body acceleration and the dynamic tyre load by 17% and 20% respectively.

scholarly journals Research on Road Adhesion Condition Identification Based on an Improved ALexNet Model

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
QiMing Wang ◽  
JinMing Xu ◽  
Tao Sun ◽  
ZhiChao Lv ◽  
GaoQiang Zong
Keyword(s):  
Machine Learning ◽  
Surface Condition ◽  
Image Features ◽  
Road Surface ◽  
Driving Safety ◽  
Error Performance ◽  
Training Time ◽  
The Road ◽  
Network Training ◽  
Proposed Model

Automotive intelligence has become a revolutionary trend in automotive technology. Complex road driving conditions directly affect driving safety and comfort. Therefore, by improving the recognition accuracy of road type or road adhesion coefficient, the ability of vehicles to perceive the surrounding environment will be enhanced. This will further contribute to vehicle intelligence. In this paper, considering that the process of manually extracting image features is complicated and that the extraction method is random for everyone, road surface condition identification method based on an improved ALexNet model, namely, the road surface recognition model (RSRM), is proposed. First, the ALexNet network model is pretrained on the ImageNet dataset offline. Second, the weights of the shallow network structure after training, including the convolutional layer, are saved and migrated to the proposed model. In addition, the fully connected layer fixed to the shallow network is replaced by 2 to 3, which improves the training accuracy and shortens the training time. Finally, the traditional machine learning and improved ALexNet model are compared, focusing on adaptability, prediction output, and error performance, among others. The results show that the accuracy of the proposed model is better than that of the traditional machine learning method by 10% and the ALexNet model by 3%, and it is 0.3 h faster than ALexNet in training speed. It is verified that RSRM effectively improves the network training speed and accuracy of road image recognition.

scholarly journals Wheel slip ratio regulation for investigating the vehicle's dynamic behavior during braking and steering input

2021 ◽  
Vol 22 ◽  
pp. 17
Author(s):  
Ali Shahabi ◽  
Amir Hossein Kazemian ◽  
Said Farahat ◽  
Faramarz Sarhaddi
Keyword(s):  
Dynamic Behavior ◽  
Fuzzy Controller ◽  
Surface Condition ◽  
Road Surface ◽  
Wheel Slip ◽  
Slip Ratio ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy ◽  
The Road ◽  
Optimal Value

In this study, the vehicle's dynamic behavior during braking and steering input is investigated by considering the quarter-car model. The case study for this research is a Sport-Utility Vehicle (SUV) with the anti-lock braking system (ABS) and nonlinear dynamic equations are considered for it along with Pacejka tire model. Regulating the wheel slip ratio in the optimal value for different conditions of the road surface (dry, wet and icy) during braking is considered as the ABS control strategy. In order to regulating the wheel slip ratio in the optimal value, an intelligent adaptive fuzzy controller that can perform online parameter estimation is considered. In this regard, the proposed controller tracks the optimal wheel slip ratio with changing the condition of the road surface from dry to wet and icy. The adaptive fuzzy controller consists of linguistic base, inference engine and defuzzifier section. The wheel slip ratio and vehicle longitudinal acceleration are selected as inputs of the controller, controller adapter and detector of the road surface condition. During braking and steering input, effective parameters of the wheel that are affected on the vehicle's dynamic behavior and its stability are investigated.

scholarly journals A Crowdsourcing Technique For Road Surface Monitoring Using Smarthphone Sensors

2021 ◽  
Author(s):  
Shahram Sattar
Keyword(s):  
Information System ◽  
Surface Condition ◽  
Road Surface ◽  
Detection Accuracy ◽  
Active Monitoring ◽  
The Road ◽  
Detection Approach ◽  
Smartphone Sensors ◽  
Road Surface Monitoring ◽  
Surface Monitoring

Road surface monitoring is a key factor in providing safe road infrastructure for road users. As a result, road surface condition monitoring aims to detect road surface anomalies such potholes, cracks and bumps, which affect driving comfort and on-road safety. Road surface anomaly detection is a widely studied problem. Recently, smartphone-based sensing has become popular with the increased amount of available embedded smartphone sensors. Using smartphones to detect road surface anomalies could change the way government agencies monitor and plan for road rehabilitation and maintenance. Several studies have been developed to utilize smartphone sensors (e.g., Global Positioning system (GPS) and accelerometers) mounted on a moving vehicle to collect and process the data to monitor and tag roadway surface defects. Geotagged images or videos from the roadways have also been used to detect the road surface anomalies. However, existing studies are limited to identifying roadway anomalies mainly from a single source or lack the utility of combined and integrated multi-sensors in terms of accuracy and functionality. Therefore, low-cost, more efficient pavement evaluation technologies and a centralized information system are necessary to provide the most up-to-date information about the road status due to the dynamic changes on the road surface This information will assist transportation authorities to monitor and enhance the road surface condition. In this research, a probabilistic-based crowdsourcing technique is developed to detect road surface anomalies from smartphone sensors such as linear accelerometers, gyroscopes and GPS to integrate multiple detections accurately. All case studies from the proposed detection approach showed an approximate 80% detection accuracy (from a single survey) which supports the inclusiveness of the detection approach. In addition, the results of the proposed probabilistic-based integration approach indicated that the detection accuracy can be further improved by 5 to 20% with multiple detections conducted by the same vehicle along the same road segments. Finally, the development of the web-based Geographic Information System (GIS) platform would facilitate the real-time and active monitoring of road surface anomalies and offer further improvement of road surface quality control in large cities like Toronto.

Improvement of Ride Comfort with Active Suspension System Using Preview Control Law

1995 ◽  
Vol 7 (4) ◽  
pp. 307-311
Author(s):  
Hideo Tobata ◽  
◽  
Takeshi Kimura ◽  
Yohsuke Akatsu
Keyword(s):  
Ride Comfort ◽  
Vertical Motion ◽  
Active Suspension ◽  
Road Surface ◽  
Vehicle Body ◽  
Vertical Acceleration ◽  
Control Force ◽  
Preview Control ◽  
Rear Suspension ◽  
The Road

It is known that the ride comfort of a vehicle equipped with active suspension can be further improved if a priori information about the road surface, i.e., preview control, is used. This paper discusses the application of preview control to the rear wheels of a vehicle with active suspension. Information about the front wheels' vertical motion is used to estimate the vertical travel of the rear wheels. Vibration transmitted from the road surface to the vehicle body through the rear suspension can be estimated from the vertical motion of the wheels. Thus, the control force that should be generated by the rear suspension actuators can be obtained. Simulation results reveal that preview control provides an accurate estimate of road force inputs, enabling the vertical acceleration of the vehicle body to be reduced for further improvement in ride comfort. The results of vehicle driving tests also confirm that the preview-control force serves to reduce the vertical acceleration of the vehicle body. Cooperation between preview control and a skyhook damper is also discussed and shown to be effective in reducing vehicle body vibration.

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