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

Abstract

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.

Deep Learning-Based Estimation of the Unknown Road Profile and State Variables for the Vehicle Suspension System

“…It is worth mentioning that while the majority of the modelbased approaches are limited to utilizing the simplified linear quarter car suspension model [13] (Figure 3 (b)), our proposed method can fully utilize all measurable in-vehicle sensor signals. Although the linear quarter car suspension model offers the advantage of low computational cost in KF algorithms, it incorporates model uncertainties as it only considers the vehicle's heave motion.…”

“…While studies have validated its usage with field test results [9], [12], several setbacks are inherent regarding its working principles. First, model uncertainty is evident as previous studies have mainly utilized the simplified linear quarter car suspension model [13], in which the nonlinearity in the suspension system is considered linear, and the pitch and roll motions of the vehicle body are neglected. Also, identified parameters vary under different driving conditions (e.g., driving speed), thus hindering the estimation performance.…”

An experimental comparison of mathematical heavy-duty city bus models to evaluate passenger ride comfort induced by road roughness

“…The root-mean-square frequency-weighted acceleration a wz induced on human mass above the rear axle is a primary location for comparison, in which similar output was observed from different measurements along the selected bus lane segment: a wz (QBM) > a wz (HBM) > a wz (FBM), as a similar trend from the literature. Each bus model can be utilized depending on the specific study purpose, such as the widely used QCS as pragmatic approaches to design individual vehicle suspension and to evaluate road roughness [38]. Based on these results, ride comfort value from quarterbus-model can be converted to that of higher DOF bus models, or vice versa.…”

Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities

“…There are also huge opportunities for CVs to contribute to infrastructure-and environmental-sensing services, such as road roughness or estimation of the International Roughness Index, bank angle, lane-marking quality, potholes, speed bumps, snow and dust coverage, etc. [152].…”