Road Roughness Estimation Based on the Vehicle Frequency Response Function

Zhang, Qingxia; Hou, Jilin; Duan, Zhongdong; Jankowski, Łukasz; Hu, Xiaoyang

doi:10.3390/act10050089

Cited by 16 publications

(4 citation statements)

References 33 publications

(69 reference statements)

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“…The IRI is one of the most reliable parameters to measure road usability [19,20]. IRI progression in the long-term is influenced by cumulative traffic load, pavement age, and the quality of paving materials [21].…”

Section: Iri Prediction Modelsmentioning

confidence: 99%

Pavement Roughness Prediction Using Explainable and Supervised Machine Learning Technique for Long-Term Performance

Sandamal,

Shashiprabha,

Muttil

et al. 2023

Sustainability

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Maintaining and rehabilitating pavement in a timely manner is essential for preserving or improving its condition, with roughness being a critical factor. Accurate prediction of road roughness is a vital component of sustainable transportation because it helps transportation planners to develop cost-effective and sustainable pavement maintenance and rehabilitation strategies. Traditional statistical methods can be less effective for this purpose due to their inherent assumptions, rendering them inaccurate. Therefore, this study employed explainable and supervised machine learning algorithms to predict the International Roughness Index (IRI) of asphalt concrete pavement in Sri Lankan arterial roads from 2013 to 2018. Two predictor variables, pavement age and cumulative traffic volume, were used in this study. Five machine learning models, namely Random Forest (RF), Decision Tree (DT), XGBoost (XGB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), were utilized and compared with the statistical model. The study findings revealed that the machine learning algorithms’ predictions were superior to those of the regression model, with a coefficient of determination (R2) of more than 0.75, except for SVM. Moreover, RF provided the best prediction among the five machine learning algorithms due to its extrapolation and global optimization capabilities. Further, SHapley Additive exPlanations (SHAP) analysis showed that both explanatory variables had positive impacts on IRI progression, with pavement age having the most significant effect. Providing accurate explanations for the decision-making processes in black box models using SHAP analysis increases the trust of road users and domain experts in the predictions generated by machine learning models. Furthermore, this study demonstrates that the use of explainable AI-based methods was more effective than traditional regression analysis in IRI prediction. Overall, using this approach, road authorities can plan for timely maintenance to avoid costly and extensive rehabilitation. Therefore, sustainable transportation can be promoted by extending pavement life and reducing frequent reconstruction.

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Section: Iri Prediction Modelsmentioning

confidence: 99%

Pavement Roughness Prediction Using Explainable and Supervised Machine Learning Technique for Long-Term Performance

Sandamal,

Shashiprabha,

Muttil

et al. 2023

Sustainability

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“…Qingxia Zhang et al proposed a road roughness estimation method using the frequency response function (FRF) of a vehicle [24]. In this study, the equation for the vehicle response, road roughness, and vehicle FRF was deduced andset up in a linear equation system; hence, road roughness can be estimated using the vehicle FRF and the measured vehicle responses.…”

Section: Road Roughness Classification Of Autonomous Vehiclesmentioning

confidence: 99%

The Braking-Pressure and Driving-Direction Determination System (BDDS) Using Road Roughness and Passenger Conditions of Surrounding Vehicles

Jeong

Son

Lee

et al. 2022

Sensors

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A fully autonomous vehicle must ensure not only fully autonomous driving but also the safety and comfort of its passengers. However, the self-driving technology that is currently completed focuses only on perfect driving and does not guarantee the safety and comfort of passengers. This paper proposes a braking-pressure and driving-direction determination system (BDDS), which computes the brake pressure and steering angle optimized for passenger safety by utilizing more diverse information than existing autonomous vehicles. The BDDS proposed in this paper consists of two modules. The road roughness classification module (RRCM) classifies the roughness of the road by using the pressure data applied to the suspension and the K-NN algorithm and computes the optimal brake pressure. The passenger recognition and sharing module (PRSM) identifies the current occupant status of the vehicle by using a body pressure sensor and CNN, shares the information with surrounding vehicles, and computes the optimal steering angle using passenger information and road information. As a result of the simulations described in this paper, the parameters of AI models were optimized. In addition, the RRCS was about 7% more accurate than the K-means clustering algorithm, and PRS was about 9% more accurate than the existing seat recognition system.

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“…In this context, real-time damage identification can be carried out by means of eigen-perturbation techniques, which are computationally more efficient than other approaches such as Kalman filtering, which becomes resource intensive in cases of many degrees of freedom [ 10 ]. According to the literature, such techniques can be adapted to detect road surface irregularities, but power spectral density (PSD) is still adopted as a convenient way to identify road status and roughness [ 11 ]. To fill the gap in the literature findings, it is important to provide RAs with tools able to reliably screen the road pavement conditions of the entire road network while being user-friendly, high performance and low cost [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Road Surface Anomaly Assessment Using Low-Cost Accelerometers: A Machine Learning Approach

Martinelli¹,

Meocci

Dolfi

et al. 2022

Sensors

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Roads are a strategic asset of a country and are of great importance for the movement of passengers and goods. Increasing traffic volume and load, together with the aging of roads, creates various types of anomalies on the road surface. This work proposes a low-cost system for real-time screening of road pavement conditions. Acceleration signals provided by on-car sensors are processed in the time–frequency domain in order to extract information about the condition of the road surface. More specifically, a short-time Fourier transform is used, and significant features, such as the coefficient of variation and the entropy computed over the energy of segments of the signal, are exploited to distinguish between well-localized pavement distresses caused by potholes and manhole covers and spread distress due to fatigue cracking and rutting. The extracted features are fed to supervised machine learning classifiers in order to distinguish the pavement distresses. System performance is assessed using real data, collected by sensors located on the car’s dashboard and floorboard and manually labeled. The experimental results show that the proposed system is effective at detecting the presence and the type of distress with high classification rates.

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Road Roughness Estimation Based on the Vehicle Frequency Response Function

Cited by 16 publications

References 33 publications

Pavement Roughness Prediction Using Explainable and Supervised Machine Learning Technique for Long-Term Performance

Pavement Roughness Prediction Using Explainable and Supervised Machine Learning Technique for Long-Term Performance

The Braking-Pressure and Driving-Direction Determination System (BDDS) Using Road Roughness and Passenger Conditions of Surrounding Vehicles

Road Surface Anomaly Assessment Using Low-Cost Accelerometers: A Machine Learning Approach

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