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

Li, Zhongxing; Yu, Wenhao; Cui, Xiaoli

doi:10.1155/2018/5131434

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…In [8], wavelet analysis was included in similar ANN for the connected vehicle environment. In [37], ANN was used with the mean square of unsprung mass acceleration divided by vehicle speed to classify road Power Spectral Density (PSD) regardless of vehicle speed and suspension parameters.…”

Section: Data-driven Methods/machine-learning Techniquesmentioning

confidence: 99%

Response-based methods to measure road surface irregularity: a state-of-the-art review

Nguyen

Lechner

Wong

2019

Eur. Transp. Res. Rev.

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Purpose: With the development of smart technologies, Internet of Things and inexpensive onboard sensors, many response-based methods to evaluate road surface conditions have emerged in the recent decade. Various techniques and systems have been developed to measure road profiles and detect road anomalies for multiple purposes such as expedient maintenance of pavements and adaptive control of vehicle dynamics to improve ride comfort and ride handling. A holistic review of studies into modern response-based techniques for road pavement applications is found to be lacking. Herein, the focus of this article is threefold: to provide an overview of the stateof-the-art response-based methods, to highlight key differences between methods and thereby to propose key focus areas for future research. Methods: Available articles regarding response-based methods to measure road surface condition were collected mainly from "Scopus" database and partially from "Google Scholar". The search period is limited to the recent 15 years. Among the 130 reviewed documents, 37% are for road profile reconstruction, 39% for pothole detection and the remaining 24% for roughness index estimation. Results: The results show that machine-learning techniques/data-driven methods have been used intensively with promising results but the disadvantages on data dependence have limited its application in some instances as compared to analytical/data processing methods. Recent algorithms to reconstruct/estimate road profiles are based mainly on passive suspension and quarter-vehicle-model, utilise fewer key parameters, being independent on speed variation and less computation for real-time/online applications. On the other hand, algorithms for pothole detection and road roughness index estimation are increasingly focusing on GPS accuracy, data aggregation and crowdsourcing platform for large-scale application. However, a novel and comprehensive system that is comparable to existing International Roughness Index and conventional Pavement Management System is still lacking.

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Section: Data-driven Methods/machine-learning Techniquesmentioning

confidence: 99%

Response-based methods to measure road surface irregularity: a state-of-the-art review

Nguyen

Lechner

Wong

2019

Eur. Transp. Res. Rev.

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“…Table 18 presents the used machine learning and the associated data collection technology. As shown in Table 18, many ML models were used to evaluate roughness using acceleration signals obtained using smartphones [102,104], vehicles' built-in sensors [45,115], mounted sensors [105,107], and simulated data [114,[117][118][119][120]. Additionally, Abohamer et al [127] developed different ML models to evaluate pavement roughness using 3D data.…”

Section: Machine Learningmentioning

confidence: 99%

“…[102] CNN Vehicle response (smartphones) [104] Extreme Gradient Boosting [115] ANN, SVM, and Random Forests Vehicle response (vehicles' built-in sensors) [45] Self-supervised Learning [105] Deep Neural Network Vehicle response (mounted sensors) [107] BiGRU [114,118,119] ANN Vehicle response (simulated data) [117] Decision Regression Tree, Random Forest, ANN [120] General Regression Neural Network (GRNN) [127] ANN, MNL, CNN 3D imaging systems [24,130] SVM, Deep Learning SAR images…”

Section: Article ML Technologymentioning

confidence: 99%

Industry- and Academic-Based Trends in Pavement Roughness Inspection Technologies over the Past Five Decades: A Critical Review

Fares

Zayed

2023

Remote Sensing

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Roughness is widely used as a primary measure of pavement condition. It is also the key indicator of the riding quality and serviceability of roads. The high demand for roughness data has bolstered the evolution of roughness measurement techniques. This study systematically investigated the various trends in pavement roughness measurement techniques within the industry and research community in the past five decades. In this study, the Scopus and TRID databases were utilized. In industry, it was revealed that laser inertial profilers prevailed over response-type methods that were popular until the 1990s. Three-dimensional triangulation is increasingly used in the automated systems developed and used by major vendors in the USA, Canada, and Australia. Among the research community, a boom of research focusing on roughness measurement has been evident in the past few years. The increasing interest in exploring new measurement methods has been fueled by crowdsourcing, the effort to develop cheaper techniques, and the growing demand for collecting roughness data by new industries. The use of crowdsourcing tools, unmanned aerial vehicles (UAVs), and synthetic aperture radar (SAR) images is expected to receive increasing attention from the research community. However, the use of 3D systems is likely to continue gaining momentum in the industry.

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“…Ground-hook control is shown in Figure 11 [41]. As illustrated in Figure 11, because the unsprung mass is connected with a damper, the groundhook control force is expressed as Equation 381 1…”

Section: Ground-hook Controlmentioning

confidence: 99%

Research on Ride Comfort and Driving Safety under Hybrid Damping Extension Control for Suspension Systems

Geng

Sun

et al. 2020

Applied Sciences

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This paper is concerned with the conflicting performances of ride comfort and driving safety for semi-active suspension systems. To alleviate this conflict, a novel hybrid damping extension control (HDEC) method is proposed. This method adopts various control methods and the weights of each method are determined by extension theory. Firstly, body acceleration and tire dynamic transformation are selected to evaluate ride comfort and driving safety performance for the semi-active suspension system and their frequency responses of passive suspension, sky-hook control, ground hook control, and S-GH (sky-ground hook) control are analyzed based on a two degree-of-freedom (2-DOF) model. Secondly, extension theory is introduced and the extension control system, which contains three modes and corresponding control algorithms, is established. In addition, the low-frequency excitation and high-frequency excitation simulations are designed to determine the parameters of the extension control system. Finally, ve-DYNA vehicle suspension model simulation is applied to prove the feasibility and effectiveness of the extension control. The simulation results show that, based on the suspension state, extension control can improve the performance of ride comfort and driving safety.

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

Cited by 7 publications

References 19 publications

Response-based methods to measure road surface irregularity: a state-of-the-art review

Response-based methods to measure road surface irregularity: a state-of-the-art review

Industry- and Academic-Based Trends in Pavement Roughness Inspection Technologies over the Past Five Decades: A Critical Review

Research on Ride Comfort and Driving Safety under Hybrid Damping Extension Control for Suspension Systems

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