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European Transport Research Review

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Open Access 01.12.2019 | Review

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

verfasst von: Teron Nguyen, Bernhard Lechner, Yiik Diew Wong

Erschienen in: European Transport Research Review | Ausgabe 1/2019

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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 state-of-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.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

acc, def, dis

Acceleration, deflection, displacement

ANFIS

Adaptive neuro-fuzzy inference system

APP

Automated pavement profiler

C, F, S

Connected vehicle, Fleet vehicle, Smartphone approach

DNN

Deep neural networks

DOF

Degree-of-freedom

DTW

Dynamic time warping

DWT

Discrete wavelet transforms

FFT

Fast Fourier transform

GPS, Data, Crowd

GPS accuracy, data fusion/aggregation, crowdsourcing platform

G-ZERO

The sensor senses a 0-g vibration

IRI

International roughness index

Kalman filter

LPA

Longitudinal profile analyser

MLs

Machine-learning techniques

NARX

Bayesian-regularised nonlinear autoregressive exogenous model

NN/ANN

Neural network/artificial neural network

P, SA, A, tyre

Passive, semi-active, active suspension system, tyre dynamics

PBPM

Vehicle probe-based pavement management

PCA

Principal component analysis

Pothole detection

PNN

Probabilistic neural network

Road profile reconstruction/estimation or road roughness classification

PSD

Power spectral density

Q, H, F, 1/5

Quarter, half, full vehicle model, 1/5 vehicle model

Roughness index estimation

Random forest classifier

RIF

Road impact factor

Sliding mode observer

STDEV(Z)

The standard deviation of Z-acc above threshold in a window

SVM

Support vector machine

Ter

Terrain classification

Transfer function

TWIT

Time-wavelength-intensity-transform

VDC

Vehicle dynamics control

Z-acc/ Z-thresh

Vertical acceleration/vertical threshold

Z-DIFF

The difference of consecutive Z-acc above threshold

1 Introduction

A rough road gives poor ride quality, increases vehicle fuel consumption and affects vehicle handling. According to a report in Britain, potholes caused more than £1 million damages to vehicles every day in 2010 [1]. Road roughness measurement is vital for transport authorities in the quest to maintain adequate ride quality for vehicles. Knowledge of road profiles also provides information for adjusting control parameters to improve ride comfort and ride handling, given the development of suspension system from passive to semi-active and active control in the automotive technology.

Generally speaking, road estimation algorithms [2] can be divided into three distinct types, namely contact measurement, non-contact measurement, and system response-based estimation. Conventional contact and non-contact measurements have been used worldwide as major pavement profiling methods. The primary contact measurement includes two categories: manual profilograph such as rods and levels, straight edges, walking profilers, and trailer-towed devices such as the Longitudinal Profile Analyser (LPA). Non-contact measurement includes inertial profilers such as the GM profilometer developed by General Motors (GM), and the Automated Pavement Profiler (APP). The advantages and disadvantages of these contact and non-contact measurements are discussed in [3‐6]. In recent years, road surface monitoring instruments have transcended from dedicated vehicles with special sensors to dedicated sensors mounted on public transport vehicles, and general-purpose sensors on privately-owned vehicles, and most recently, smartphone-enabled automated monitoring of road infrastructure [7]. This development is driven by response-based methods to indirectly assess road roughness condition using measurements of displacements, velocities, and accelerations of vehicle components, resulting in cost reduction for labour and equipment as compared with direct contact/non-contact profiling [8]. This has led to the emergence of Probe Data Performance Management (PDPM) or Vehicle Probe-based Pavement Management (PBPM) for assessing pavement quality through probe data [9]. There are three system structures by way of connected vehicle approach, fleet vehicle approach and smartphone approach. Basically, road excitation can be estimated using onboard sensors (accelerometers, gyroscopes) for individual or a combination of three key functions as follows (see Fig. 1):

Road Profile Reconstruction/estimation or road roughness classification - PR (e.g. Power Spectral Density – PSD), in which fast computation (e.g. in second) adapts vehicle parameters to road roughness levels;

Potholes Detection – PD, which detects potholes, manholes, road defects where the precise localisation is of importance; and

Roughness Index Estimation – RE (e.g. International roughness index – IRI or new index) for pavement maintenance where roughness data is often aggregated for a certain segment length.

A brief overview of approaches using dedicated sensors and smartphone sensors can be found in [10, 11], yet a comprehensive review is lacking. Herein, in this literature review paper, around 130 articles have been reviewed focusing on the methodologies but not on theories, empirical insights or conceptual model [12]. The objectives and contribution of this review are threefold. Firstly, an examination of the state-of-the-art response-based methods is conducted to provide an overview of their developments within the last 10 years. This provides a comprehensive understanding of the diversity of on-going and dominant methodologies being used. Secondly, the key pros and cons of different methods, e.g. signal processing, data-driven, threshold-based, transfer function, are highlighted. Lastly, key focus areas on the estimation of road surface irregularity are proposed as opportunities for further studies such as the inclusion of air-suspension system, improvement of current machine learning algorithms or further development of the fleet vehicle approach. The results of this review serve to shed light and provide orientation for the research community on system response-based estimation.

Figure 2 illustrates a topology of approaches to measure road surface irregularity focusing on system response-based methods with detailed applications for vehicle dynamics control (VDC) in dealing with PR for adjusting vehicle parameters to improve ride comfort and ride handling; and PBPM utilising portable onboard sensors and smartphones for PD and RE in citywide network.

The methodology for gathering “response-based methods literature database” is presented in the next section. PR algorithms for VDC are then described, followed by PD and RE algorithms for PBPM. The discussion, conclusion and outlook section reports the main results of this review study and proposes research and development gaps deserving of further study.

2 Literature data retrieval method

Available articles regarding response-based methods to measure road surface condition were collected mainly from “Scopus” database [13] and partially from “Google Scholar” [14]. Articles of focus are those published by international journals and high-quality conferences. The first round of online search was conducted using the following keywords: ((“road roughness” OR “road profile” OR “pothole”) AND (accelerometer OR response) AND (estimation OR classification OR detection)) AND PUBYEAR > 2005, using Scopus’ default search settings: article titles, abstracts and/or keywords. The search period is limited to the recent 15 years since an initial investigation found that studies on the topics mostly started at around 2006, with predominant numbers in the past 10 years (see Fig. 3b).

A total of 161 documents were obtained from the various field of studies, of which 86 are published journal articles, 3 are articles in press, 1 is a book chapter and 71 are conference papers. All retrieved documents were further analysed in which 87 documents were removed as being insufficiently related to the main scope of VDC or PBPM nor the main functionalities of system response-based estimation (PR, PD or RE); these rejected documents are mostly related to bridge-vehicle interaction. Relevant references (56) were retrieved and included in the analysis (see Fig. 3a). The additional literature that was missed in the direct search is due to various technical terms being used in these documents such as road anomaly, abnormal section, impact, defect, bump, irregularity, failure, damage (instead of ‘pothole’) or sensing, measurement (instead of estimation, classification, detection). Among the 130 reviewed documents, 37% are for road profile reconstruction, 39% for pothole detection and the remaining 24% for roughness index estimation.

3 Results

3.1 Road profile reconstruction/estimation for vehicles dynamics control

Profile reconstruction/estimation (PR) is essential for vehicle dynamics control (VDC). However, control algorithms are dependent on vehicle suspension types, be it passive, semi-active or active, to formulate the corrective dynamics behaviours [15]. PR algorithms for VDC can be classified into three main approaches: 1) model-based methods or observers/estimators, 2) data-driven methods/machine-learning techniques, and 3) frequency response functions/transfer functions and others. These are described in the following sections.

3.1.1 Model-based methods (observers/estimators)

Kalman filter/estimator (KF) and sliding mode observer (SM) are the most commonly-used methods since a long time. Three standard KFs are the linear KF for linear cases, the extended KF for a non-linear relationship, and the unscented KF for strong nonlinearities. Initially in 2011, the linear quarter-car model was developed to implement the KF method [16] that needed measurements of the suspension deflections, the body position and acceleration. In [17], an improved KF was developed to include the vehicle sprung mass change, and in [18] an augmented KF was developed to make use of all the available sensors. The PR is implemented with the modified KF framework in [19] for the non-linear spring-damper system to localise autonomous vehicle position. Unfortunately, for all KF methods, the tuning of the covariance matrix is usually done heuristically which effects the estimation results caused by the deterioration and loss of information. To overcome this drawback, an algebraic estimator was developed in [20], by updating the covariance matrix according to the change of road roughness [21], or by applying the adaptive KF and adaptive super-twisting observer (AKF-ASTO) algorithm in a new estimator [22].

Regarding other observer approaches, the most common method is the sliding mode observer (SM) considering the road profile as unknown inputs to be estimated. A 16-DOF full-car model was firstly used to develop the SM based on the vertical motion of the vehicle [5]. A researcher [23] then developed a second-order SM to avoid the assumption of constant velocity, while another model-based observer was developed to compensate for the chassis dynamics for minimising its interaction effect [6]. The higher-order SMs using adaptive super-twisting observer based on a nonlinear quarter-car model were also developed in [24] for PR, and in [25] for PR and tyre friction estimation simultaneously. The combination of sliding mode observer and adaptive Kalman filter for PR related to tyre dynamics can be found for active suspension control in [26]. Other methods of control theory using an adaptive observer with the Q-parameterisation method have shown their validity and feasibility [27] and the extensions in [28, 29] with detailed synthesis and experimental validation. Compared to other methods such as KF, the Q-parametrization method provides better performance and is suitable for real-time implementation due to less computing cost and implementation complexity.

Another state observer can be found in [30] to use the overall response of the preceding vehicle(s) to generate preview controller information for follower vehicles. An H_∞ observer was adopted and found to be feasible for real-time implementation but required knowledge of many vehicle parameters [31], while a jump-diffusion process estimator can perform PD and PR simultaneously [32]. Although these types of estimators can work effectively for active suspension system control, extensive modelling is required as the main drawback as well as the problem of speed variation.

3.1.2 Data-driven methods/machine-learning techniques

The emergence of machine-learning techniques (MLs) has motivated researchers to focus on various ML algorithms to measure road surface irregularity, as reported in more than half of the reviewed documents. Among them, Neural Network (NN)/Artificial Neural Network (ANN) and Support Vector Machine (SVM) are the most common methods. In 2010, a study [33] used a Bayesian-regularised nonlinear autoregressive exogenous model (NARX) for PR based on the acceleration from a linear half-vehicle model. The ANN-based methodology has been applied for road surface condition identification on mining vehicles and mining roads [34], and for the Land Rover Defender 110 [35]. Similar ANN can be found in [36] using seven vehicle acceleration variables as inputs. To improve estimation efficiency, different techniques/algorithms have been implemented along with ANN. 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.

To classify different road types/terrains (e.g. brick, gravel, grass), ANN and principal component analysis (PCA) were used in combination with image processing [38], or SVM with PCA [39]. To remove the speed dependence from terrain classification, SVM was combined with wavelet analysis of acceleration data [40], or SVM with spatial frequency component analysis by Fast Fourier Transform [41].

Apart from ANN and SVM, other sophisticated MLs were developed and often combined with other techniques for VDC. Deep Neural Networks [42] and Probabilistic Neural Network classifier [43] were proposed by using measurable system responses. The Adaptive Neuro-Fuzzy Inference System - ANFIS road classification method was proposed using wavelet analysis based solely on sprung mass acceleration [44]. ANFIS classifier was found to be better than other methods in [45], and ANFIS was combined with KF for VDC of semi-active suspension in [21, 46]. PNN classifier using wavelet analysis showed better performance than ANFIS and NARX methods. The combination with PNN classifier and AKF-ASTO [22] adaptively changes the process noise covariances Q and R for the KF, resulted in higher accuracy than existing KF method. Random forest classifier (RF) was used to combine information from both time and frequency domains for a controllable suspension system in [2], while the RF was combined with transfer function to develop a speed independent road classification strategy in [47]. Most recently, independent component analysis as a simple and fast method was developed in [48], and various MLs were compared in [49].

3.1.3 Transfer functions and other techniques

The transfer function (TF) was first used by Gonzalez in 2008 [50] to estimate road PSD based on the relationship between the road surface and vehicle acceleration via a TF as Eq. 1:

$$ H (\Omega)=PSD_{acc}(\Omega)/PSD_{road}(\Omega)$$

(1)

where PSD_acc(Ω) and PSD_road(Ω) are the PSD for a frequency Ω due to vehicle accelerations and road profile, respectively.

The road can be classified according to ISO 8608 [51] based on PSD_road estimated from the PSD_acc of the axle or body acceleration measurements [50]. In [52], similar results have confirmed the efficiency of the TF approach, and in [53] the TF was extended to a full-vehicle model to estimate road PSD regardless of vehicle speeds. From another point of view, dynamic tyre pressure sensor was used to estimate road profiles based on an assumption of a linear relationship between road surface profiles and tyre pressure via a TF [54].

Regarding other methods, a numerical optimisation technique can be found in [55] that employs Monte Carlo simulations to obtain the optimal PR, but it is costly for computing. The method of control-constraints was proposed [56] that focuses on tyre dynamics and requires solving differential-algebraic equations. A modulating function technique [57] can fulfil the real-time and noise suppression requirements with the focus particularly on off-road vehicles. In [58], Bayesian estimator was proposed regardless of vehicle models; but a priori information of the road is required. In addition to acceleration measurements, PR can be done by microphones to measure tyre noise [59]; however, a robustness study is needed to reduce signal contaminations.

3.1.4 Summary of methods for road profile reconstruction/estimation

Table 1 lists the related model-based methods where most of them use a passive suspension system and quarter-vehicle model while fewer use active suspension system. Q-parameterisation has demonstrated its better performance than other methods, with less parameter information required after experimental validation using passive, semi-active and active suspension systems. The pothole detection does not gain much research interest with only 2 relevant studies. Studies on data-driven methods are listed in Table 2 and similarly most studies use a passive suspension system and quarter-vehicle model. Together with road profile reconstruction, the functions of pothole detection (2 studies), roughness index estimation (1 study) or terrain classification (4 studies in which 3 are from the same first author) can be found. Since the first ML emerging from NARX in 2010, recent research continues to improve the algorithms by increasing estimation accuracy and using less parameter information such as the ANFIS (only sprung mass). Research related to speed independence has shown the potential for large-scale application with both offline-online phase classification steps such as the speed independent road classification strategy - SIRCS. Studies on transfer function and other methods are listed in Table 3 for road profile reconstruction only without consideration of pothole detection or roughness index estimation, in which all the algorithms were developed using the passive suspension system. The sophisticated modelling of other methods has negated them from the real-time or online application.

Table 1

Summary of model-based methods for road profile reconstruction function

System name/by	Model-based approach	Additional	Suspension			Vehicle model			Main parameter
System name/by	Model-based approach	Additional	P	SA	A	Q	H	F
[16]	KF		✔			✔			body position and acc, suspension def
[17]	improved KF		✔			✔			sprung acc, suspension def
[18]	augmented KF		✔			✔		✔	suspension dis, unsprung, sprung acc
[19]	modified KF		✔	✔		✔	✔	✔	vertical dis of the tire-road contact points, longitudinal acc
[5, 23]	SM, second-order SM		✔					✔	wheels and chassis
[6]	SM	PD	✔			✔			chassis
[24]	higher-order SM				✔	✔			sprung mass dis and velocity
[25]	higher-order SM				Tyre	✔			random road profile, the longitudinal friction force, and the engine friction
[26]	SM + AKF				Tyre	✔		✔	spring def, wheel acc, tire road contact acc
[27]	Q-parametrization				✔	✔			sprung mass position
[28]	Q-parametrization			✔		✔(1/5)
[29]	Q-parametrization		✔			✔
[20, 30]	Algebraic estimator, state observer		✔			✔			sprung mass and unsprung mass vertical dis, suspension def
[31]	H∞ observer				✔	✔(1/5)			sprung acc, suspension def, unsprung mass motion
[32]	Jump-diffusion estimator	PD	✔				✔		wheel excitation

Table 2

Summary of data-driven methods for road profile reconstruction function

System name/by	Machine learnings	Additional	Suspension			Vehicle model			Main parameter
System name/by	Machine learnings	Additional	P	SA	A	Q	H	F	Main parameter
[33‐35]	ANN (NARX)	PD	✔				✔		sprung, axle, body
[36]	ANN		✔					✔	wheels and chassis
[8]	ANN + wavelet DWT)	RE(IRI)	✔			✔			sprung mass
[37]	ANN + ADV		✔	✔				✔	unsprung mass
[38]	ANN + image processing + PCA	Terrain	✔			✔			wheel acc, speed
[39‐41]	SVM+ PCA, FWT, FFT	Terrain	✔			✔			wheel acc, speed
DNNs classifier [42]	Deep NNs			✔				✔	sprung, unsprung, rattle space
PNN classifier [43]	PNN + WPT			✔		✔			sprung, unsprung, rattle space
ANFIS classifier [44]	ANFIS			✔		✔			sprung mass
[45]	ANFIS, RLS, GMDH			✔		✔			sprung, unsprung, rattle space
ANFIS+AKF [21]	ANFIS + Kalman filter				✔	✔			sprung mass
AKF-ASTO [22]	PNN + Kalman filter					✔			sprung, unsprung
[46]	ANFIS + MOOP + NSGA-II			✔		✔			sprung mass
[2]	RF + WPT				✔	✔	✔		sprung, unsprung, speed
SIRCS [47]	RF + TF, decision procedure		✔			✔			unsprung mass
[48]	Independent Component Analysis		✔			✔	✔	✔	chassis, suspension
[49]	Various MLs + TF	PD	✔					✔	axle or body, speed

PCA, WPD, WPT, DWT, FWT: Principal Component Analysis, Wavelet Package Decomposition, Wavelet Package Transformation, Discrete Wavelet Transform, Fast Wavelet Transform.

RLS, GMDH, ADV: Recursive Least Square, Group Method of Data Handling, the mean square of unsprung mass acceleration divided by vehicle speed.

Table 3

Summary of TF and other methods for road profile reconstruction function

System name/by	TF and others	Suspension			Vehicle model			Main parameter
System name/by	TF and others	P	SA	A	Q	H	F
[50]	TF	✔				✔		axle or body
[52]	TF	✔			✔			unsprung mass acceleration
[54]	TF	✔						tyre pressure
[53]	TF + time span	✔					✔	axle or body
[55]	Cross-entropy	✔				✔		sprung and unsprung acc
[56]	Control-constraints	✔					✔	tire dynamics
[58]	Bayesian parameter							rear wheel acc, veh response, speed
[59]	Microphone	✔			✔			tyre noise and axle acc
[57]	Modulating function technique	✔					✔	accelerometer, spring dis and orientation

In summary, various methods have been developed for PR (48 studies) and several include additional functions for PD (4/48 or 8.3%) and RE (1/48 or 2%), in which TF and other methods have focused on PR (9/48 or 19%) only (see Fig. 4). A high number of studies use quarter-vehicle model (29/48 or 60%) and passive suspension system (32/48 or 67%), in which TF and other methods mostly use passive system (8/9 or 89%). Starting from the first developed Kalman filter, sliding mode observer, artificial neural network and transfer function methods in the 2010s which require many vehicle parameters but fewer accuracy levels, recent methods are focusing on fast computation with fewer parameters for online and real-time application. The combination of different techniques has resulted in higher estimation performance such as machine learning and feature extraction, or machine learning and Kalman filters.

3.2 Pothole detection and roughness index estimation

The three approaches in PBPM has been classified as: connected vehicle approach (uses OEM-installed accelerometers, sensor hardware and standardised onboard vehicle) fleet vehicle approach (uses semi-permanent, non-stock accelerometers in a fleet of agency-owned vehicles, supplemented by GPS units) and mobile device approach (uses accelerometer-equipped mobile devices to gather and transmit roughness information to a central database) [9]. The latter approach using smartphone sensors as the concept of “citizen as sensors” in [60] or “citizen engineer” [61] has received much research interest in recent years, followed by the connected vehicle and the fleet vehicle (which can be grouped into one dedicated sensor onboard approach). Regardless of applications, the methodology can be classified into three groups: acceleration thresholds or threshold-based methods, signal processing and machine-learning techniques.

3.2.1 Threshold-based methods

Threshold-based methods are the most straightforward approaches for PD detection by processing mainly the vertical acceleration (Z-acc) or in combination with other direction acceleration (x and y) and gyroscopes. A researcher [62] has proposed four indices in which the Z-THRESH was further modified [63] to build a cloud computing system: Z-THRESH (from vertical vibration), Z-DIFF (from the difference of consecutive Z-acc above threshold), STDEV(Z) (as the standard deviation of Z-acc above threshold in a window), and G-ZERO (whether the sensor senses a 0-G vibration). Similar STDEV(Z) can also be found in [64] and to develop a bump index in [65]. Other acceleration thresholds are used to classify three relative rough road levels [66] or severity levels of potholes [67, 68] and to characterise road bumps [69]. Thresholds of Z-acceleration and ultrasonic data were combined in [70] while the Z-jerk as the “rate of change of acceleration” is used in Cyber-physical system [71]. However, how to set up correct thresholds is challenging under the influence of vehicle speeds, suspension parameters as well as sensor location and orientation. Furthermore, only pothole detection alone is not sufficient for real application, as transport authorities care about roughness index estimation as well for road surface maintenance.

3.2.2 Signal processing

To overcome the drawbacks of the threshold-based methods, various signal processing filters have been used. Researcher [72] further processed Z-acc by simple filters and Gaussian model-based algorithm to detect the severity of potholes and differentiate humps and potholes. A study in [73] combined Z-THRESH and G-ZERO and adopted a spatial interpolation method to obtain precisely pothole locations. Fuzzy logic was used to detect and recognise the speed bumps from vehicle speed and Z-acc variance [74]. Time-frequency analysis was used such as the Discrete Wavelet Transform to estimate gravel roads ride quality, detect the location and the severity of surface potholes [75], or the Gabor transform to estimate road roughness condition in combination with image processing for PD [76]. In [77], a greedy heuristic approach for an optimal mobile sensor placement maximises the total length of the road inspected by sensors.

Frequency filter, speed filter and small peaks filter were used to develop the vertical acceleration impulse that corresponds to a “high-energy event” on the road surface in UNIquALroad [78, 79]. Dynamic Time Warping – DTW detects pothole by using the pattern-matching technique independent of time and speed [80]. Similar to DTW, the Smartphone Probe Car system was developed using a new road anomaly indexing heuristic which is adaptive to vehicle dynamics [81].

To evaluate road roughness IRI, the well-known regression relationships between PSD with IRI was investigated in [82, 83], so do the root-mean-squared acceleration (RMS) and IRI in [84, 85]. A compact road profiler and ArcGIS to measure and evaluate road roughness condition was introduced in [86]. Filter and Fast Fourier Transform (FFT) were used to estimate IRI from smartphone data under realistic setting (e.g. inside pockets) based on the approximate proportion of spectrum magnitude and road IRI [87, 88]. The inverse pseudo-excitation method offers a new approach to estimate IRI independent of the travelling speed, road roughness grade, and vehicle type [89]. The RMS acceleration was further studied to detect potholes using speed filter and Z-axis filter in Pothole Patrol, and to develop new roughness index (IRI-proxy) depicting overall road quality [60]. Based on the relationship of PSD between road surface and vertical acceleration, parameters of road profile can be evaluated using Maximum Likelihood-based estimation [90], or using linear predictive coding by averaging the power of the prediction error [91, 92]. In [93], a recursive multiscale Correlation-Averaging algorithm was developed to deal with the uncertainty/noise such as GPS inaccuracies, driving path variation and errors from the distance-measuring devices.

Regarding new roughness index, a speed-independent road impact factor - RIF (individual vehicle) and its corresponding time-wavelength-intensity-transform – TWIT (vehicle groups) for connected vehicles were established using advanced signal processing in [94]. Further studies were conducted intensively to investigate and validate the RIF regarding sampling rate selection [95], localisation [96, 97], RIF-IRI proportionality [98], deterioration forecasts in consideration of suspension parameter variances [99], stop-and-go conditions [100], and wavelength sensitivity [101].

3.2.3 Machine learning techniques

With more data availability, machine learning techniques (MLs) have been utilised in PD and RE functions while noting that most of them were developed for the PD. The abovementioned Z-THRESH is similar but simpler than Z-peak in Pothole Patrol [102], Nericell [103] and TrafficSense [104], which used specific algorithms to filter and to cluster the collected data. Based on Pothole Patrol, further analysis to differentiate pothole and bump-road cases in [105], or to develop the PRISM platform for remote sensing [106]. For the same purpose, a supervised learning approach based on temporal classification was undertaken in [107]. Based on Pothole Patrol, P³ can infer the depth and length of the pothole by adopting a one degree-of-freedom (DOF) vibration model as well as perform a self-learning vibration recovery algorithm [108]. A clustering ML was used to cluster potholes with an adaptive detection threshold and learning rate update in CRSM [109, 110] after using pothole filters in Pothole Patrol. K-mean clustering was used in [111] and additional Random Forest (RF) classifier in [112]. Another study [113] developed an online road roughness classification system using bicycles instrumented with smartphones embedded with the K-Nearest-Neighbour and Naive Bayes algorithm.

Among MLs, support vector machine (SVM) is used most frequently, and it is often combined with feature extraction methods as multiple classifiers. In [114], SVM was used to detect road anomalies by processing the data collected from a motorcycle-mounted tri-axial accelerometer and further classify road surface condition using unsupervised ML. Recently, SVM and Dynamic Time Warping algorithm were developed in [115] to identify aggressive driving events, road bumps and potholes for cycling. Another improvement was included in [116] where the gyroscope around gravity rotation was used. SVM and wavelet analysis were also used in RoADS [117] to classify the road anomaly into three event classes: severe, mild and span, and in [118] to detect road anomaly based on driver attitudes toward the speeds and turnings. SVM and Fast Fourier Transformation were used in [119] to remove the speed dependence and to label road anomaly. Another study in [120] combined SVM and Wavelet Package Decomposition to detect potholes with low computing cost. In Wolverine [121], the smartphone accelerometer data is used to detect braking events and bumps using K-means clustering and SVM. In [122, 123], SVM was trained using extensive data set from CarSim vehicle simulation as well as experiment, applying for under-sampled vehicles sensor problems and multi-lane pothole detection. In [124], a virtual road network inspector was built based on SVMs to detect potholes using accelerometers mounted to the front and rear axles of the buses.

The comparison of different MLs was conducted in several studies to find the best ML. In [125], a data mining approach was developed to compare the performance of five algorithms for PD. By adopting the framework of this study, a study [126] used RF for its best performance to develop a cloud-based Road Anomaly Service architecture in which PCA was used for feature extraction. PCA was also used in [127, 128] after NN and RF classification were compared to develop a street defect classifier to select NN for its better performance. RMS thresholds were set as a triggering condition for data logging condition and a new street defect level (from 0 to 1) to evaluate the road segment condition. In RoadSense [129], Decision Tree (DT) was designed and compared with SVM and Naïve Bayes algorithm after feature extraction. In Pothole lab [130], a new SVM(Z) and Swarm indices were developed to compare with the four thresholds in [62], Nericell, Pothole Patrol, and PERT [119]. Backward feature elimination was used in [131] to select the optimal set of features for different classification models while in [132] the forward selection and backwards elimination process was performed showing better performance than existing approaches.

Besides CRSM system for IRI estimation, MLs were used in [133] where the authors used smartphone sensor data for training a feature-based prediction model and compared with the road condition from official IRI measurements of the road surface. Another researcher [134] applied NARX ANN to estimate IRI from the connected vehicle after investigating vehicle suspension characteristics and its speed in [8]. In [7], the mean-absolute-value of the Z-acc for every 100 m was sensed by a smartphone on a motorbike, and a fuzzy classifier from a server was used for RE.

3.2.4 Summary of methods for pothole detection and road roughness estimation

Studies on threshold-based methods are listed in Table 4. Given the simplicity of this method based on true positive and false positive of the detection rate, the threshold values may vary due to different factors which make this method not being feasible to be used in real scenarios and large-scale implementation. Table 5 lists the studies on signal processing methods, in which not only the methods of accurate PD and RE but also further concerns on GPS data noise/inaccuracy, sensor and smartphone installation/direction, data fusion/aggregation and crowdsensing system/platform were considered. Among them, the adaptive thresholds in Smartphone Probe Car and Smart patrolling, as well as the IRI-proxy, SmartRoadSense and UNIquaALroad system are found to be promising for large scale application. RIF and TWIT are also potential replacements of IRI in the context of connected vehicle environments. As for ML methods recent studies are listed in Table 6. MLs have attracted many studies resulting in high performance in which PCA plays an important role in feature extraction for the training process. CRSM [109, 110], the system in [122, 123] and another in [133] are promising systems for large scale application.

Table 4

Summary of threshold-based methods for pothole detection and roughness index estimation

System name/by	Thresholds	Function		Approach
System name/by	Thresholds	PD	RE	C	F	S
[62], [63]	Z-thresh, Z-diff, Stdev(z), G-zero	✔				✔
BusNet [64]	std of filtered Z-acc	✔			✔
Bump Recorder [65]	Z-acc, bump index	✔				✔
[66]	Z-acc	✔	Relative			✔
Smart Pune [67]	Z-acc, skid, accident, braking	✔		✔
[68]	Z-acc for severity levels	✔				✔
[69]	Z-acc pattern	✔				✔
Cyber-physical system [71]	Z-jerk	✔				✔
[135]	0.1 g threshold	✔				✔
PoDAS [70]	Z-acc, ultrasonic	✔		✔

Relative: Pothole-based roughness index.

Table 5

Summary of signal processing methods for pothole detection and roughness index estimation

System name/by	Signal processing	Function		Approach			Additional
System name/by	Signal processing	PD	RE	C	F	S	GPS	Data	Crowd
UNIquaALroad[78, 79]	high-energy events	✔				✔		✔	✔
Smart patrolling [80]	filter + DTW (adaptive)	✔				✔		✔	✔
Smart Probe Car [81]	anomaly index heuristic (adaptive AI)	✔				✔	✔	✔	✔
[72]	Z-acc, Gaussian model	✔				✔
[73]	Z-thresh, G-zero combined	✔				✔		✔
[74]	Fuzzy logic	✔				✔
[75, 76]	time-frequency analysis	✔				✔
[77]	Greedy heuristic algorithm	✔			✔		✔	✔
[90]	Maximum Likelihood-based	✔		✔
RCM-TAGPS [82, 83]	PSD + empirical formula		IRI	✔
[84]	RMS acceleration		IRI	✔
[85]	RMS acceleration		IRI			✔
STAMPER [86]	filter + IRI		IRI	✔
[87, 88]	Filter + FFT		IRI			✔
IPEM [89]	Inverse pseudo-excitation method		IRI	✔
[60]	IRI-proxy	✔	IRI-proxy			✔		✔	✔
SmartRoadSense [91, 92]	PSD + Linear Predictive Coding		Relative			✔	✔	✔	✔
[93]	Correlation-Averaging Algorithm	✔	✔	✔			✔	✔
RIF [95‐101]	RIF-transform, TWIT	✔	New	✔			✔	✔

Table 6

Summary of ML methods for pothole detection and roughness index estimation

System name/by	Machine learning	Function		Approach			Additional
System name/by	Machine learning	PD	RE	C	F	S	GPS	Data	Crowd
Pothole Patrol [102]	Clustering + training detector	✔		✔
Nericell [103], TrafficSense [104, 105]	Z-peak method/ Clustering + training detector	✔				✔
PRISM [106]	Z-peak method + training detector	✔				✔			✔
[107]	supervised ML	✔				✔
P³ [108]	Clustering + training detector	✔				✔		✔
PADS [111]	K-mean clustering	✔		✔
BDS [112]	K-means clustering + RF	✔				✔
[113]	Naive Bayes algorithm + K-nearest-neighbor		Relative			✔
[114]	SVM + unsupervised ML	✔	Relative			✔
D&Sense [115]	SVM + DTW	✔				✔			✔
RoadMonitor [116], RoADS-based [117, 118]	SVM, SVM + SWT	✔				✔
[119]	SVM + FFT, cross validation	✔				✔	✔
[120]	SVM + WPD, feature selection	✔				✔
Wolverine [121]	SVM + K-means clustering	✔				✔
[122] [123]	SVM + data filter, sliding window, greedy forward feature selection	✔		✔			✔	✔	✔
VRNI [124]	SVM + filter, moving window, feature extraction	✔			✔
CRISP-DM-based [125, 126]	various algorithms comparison	✔	Relative			✔
[127, 128]	various algorithms comparison	✔	Relative	✔			✔	✔
RoadSense [129], Pothole Lab [130]	various algorithms comparison	✔				✔			✔
[131]	various algorithms comparison	✔				✔
[132]	various algorithms comparison	✔		✔				✔
CRSM [109, 110]	iGMM clustering	✔	IRI		✔			✔	✔
[133]	SVM + WPD, Random forest		IRI			✔	✔	✔	✔
[134]	ANN + feature selection		IRI	✔				✔
[7]	Fuzzy classifier	✔	Relative			✔		✔	✔

Relative: Pothole-based roughness index;

ANN, SVM, RF, DT: Artificial Neural Network, Support Vector Machine, Random Forest, Decision Tree;

PCA, WPD, DWT: Principal Component Analysis, Wavelet Package Decomposition, Discrete Wavelet Transform.

In summary, the diversity of methods and systems have been described in over 80 reviewed articles for the main functions of PD (50/80 or 63%) and RE (30/80 or 27%). Many algorithms can perform both PD and RE (20/80 or 20%). The same number of studies use MLs and signal processing (34 each or 41%) whereas threshold-based methods are used mostly for PD (8%). MLs received more research interest than other methods for PD (26/50 or 52%). In contrast, signal processing is preferred for RE (22/30 or 73%) especially for IRI estimation, in which 11/22 studies (50%) are original algorithms while others are further development or application (Fig. 4). Over the studies related to RE, 6/30 (20%) is about the relative roughness index, 14/30 (47%) for IRI estimation, 2/30 (7%) for IRI-proxy estimation and 8/30 (27%) for the new roughness index (RIF and TWIT). There are only 6 studies (7%) related to fleet vehicle approach, 23 studies for the connected vehicle (28%) and 53 studies for smartphone approach (65%). The problems of GPS accuracy, data aggregation and crowdsourcing have been considered in many studies using signal processing (9/21 studies) and ML (13/30 studies), aiming at supporting the emergence of crowdsourcing-based road surface monitoring.

4 Discussion, conclusion and outlook

Different methods present different levels of complexity, precision and computing intensiveness. Across all the reviewed documents and methods, it is recognised that data-driven methods/MLs are increasingly being used for all the functions in PR, PD and RE (see Fig. 4), as well as the usage of the passive suspension system and quarter-vehicle model due to their modelling simplification. Recent studies have shifted towards RE as shown in the time series graphs, in which signal processing techniques have been preferred for RE given the ability to achieve advanced functionalities such as adaptive thresholds or data fusion. Regarding the function of PR for the individual suspension system, it is more comprehensive to integrate PR for suspension control with variable uncertainty, but more challenges will occur on the knowledge of vehicle dynamic characterisation. Whereas to deal with PD and RE for group of vehicles (fleet or connected vehicle) and “citizen sensor” concept in the large-scale society, the issues of GPS accuracy, data fusion (e.g. the aggregation of sensor data or vehicle suspension types) and crowdsourcing will be challenges to the development of appropriate algorithms/systems. So far, several established algorithms/systems have solved these issues successfully.

In summary, the development of response-based methods to evaluate road surface irregularity has attracted research interests from both automotive technology and pavement engineering, aiming at the three main functions of Road profile reconstruction (PR), pothole detection (PD) and roughness index estimation (RE). The review of about 130 articles on this topic has revealed the diversity of recent approaches mostly within the recent decade. At first, the present study describes the algorithms used for PR including model-based methods, data-driven methods, transfer functions and others. Then, related algorithms for PD and RE are described including the threshold-based, signal processing and machine-learning methods. Following this, all reviewed documents and discussion are summarised on their advantages and disadvantages (see Table 7) which should be beneficial for further research in this field.

Table 7

Advantages and disadvantages of response-based methods

Response-based methods	Advantages	Disadvantages
1. Road profile reconstruction
1.1. Model-based approach	can deal with unforeseen situations that are not included in the data-driven training datasets.	- an accurate model is required - not all required response information is measurable - often only time domains
1.2 Kalman filter/estimator	convenient, fast and simple	- a priori information about model errors - the tuning of the covariance matrix is usually done heuristically
1.1.2 Observer	can include tyre dynamics	generally required knowledge of many vehicle parameters
i. Sliding mode observer	- convergence of the errors	rather complicated for practical application
ii. Q-parameterisation	- less computing cost and complexity for real-time implementation - better performance than KF	- the problem of extensive modelling - the sensitivity to speed variation in almost methods
iii. Algebraic estimator iv. H∞ observer v. State observer vi. Jump-diffusion	- can work effectively in the framework of the active suspension system - overcome the drawbacks of KF
1.2 Data-driven approach (MLs)	- can use fewer parameters (e.g. only sprung or unsprung mass) - various ML techniques to be applied - does not require excessive system characterisation - required fewer analytical skills than parametric model	- impractical for an online road estimation due to computationally costly training datasets (e.g. 4655 s are required to train the ANN-based moded)
1.2.1 Only MLs (e.g. ANN)	- able to detect potholes	- spatial frequency only - many vehicle parameters - not high accuracy and sensitivity to speed variation
1.2.2 Combined MLs and others	- higher accuracy and performance - feasible for speed independent classifiers
i. with feature selection (e.g. WPT, FFT, PCA)	- can combine both time and frequency domains - able to classify terrain conditions	further complex modelling and understanding vehicle dynamics control mechanism
ii. with KF	determination of the process noise variance before estimation
iii. with TF	- speed independent classifier with less training effort - able to detect potholes
1.3 Transfer function and others	required fewer parameters than the model-based approach
1.3.1 The transfer function (TF)	- easy, convenient and fast - frequency domain only	- not directly yield the expression of the excitation - limited to a constant speed (can be eliminated when combined TF with small time span)
1.3.2 Others
i. Cross-entropy	using only sprung and unsprung mass accelerations	too much computing time
ii. Control-constraints	non-linear and complex models	remains costly
iii. Bayesian parameter	low cost regardless of vehicle models	a priori information of the road is required
iv. Microphone	feasible for the combination of techniques	the susceptibility to signal contaminations
v. Modulating function	fulfil the real-time and noise suppression requirements	particularly for off-road vehicles
2. Road roughness estimation and pothole detection
2.1 Threshold-based methods
2.1.1 Thresholds only	simplest methods (for PD purpose) with fix thresholds	threshold value varies with different types of smartphones, roads, vehicles, the condition of vehicles.
2.1.2 Combined thresholds and others	overcome drawbacks of the threshold-based methods
i. with signal processing approaches	- able to detect the severity of potholes, differentiate potholes and humps
ii. with MLs to train detectors	- clustering of different road anomalies with simple algorithms	training datasets required which are not able to collect in some cases
2.2 Signal processing	- able include both PD and RE in the same system - deal with GPS errors, data aggregation, device installation and orientation, crowdsourcing - higher performance and accuracy - suitable for data aggregation regardless of different configuration (e.g. velocity, orientation, suspension)	complicated analysis
2.2.1 PSD and RMS acceleration	calculate IRI value	not able to detect a pothole
2.2.2 RIF transformation	- feasible for connected vehicles - both PD and RE considering a fleet of vehicles	advanced signal processing
2.2.3 Adaptive threshold (e.g. DWT)	less training effort as compared to MLs
2.3 Data-driven approach (MLs)	- various techniques to be applied to select the best alternative - easier to implement in the smartphone for crowdsourcing	a huge amount of training datasets required which are not able to collect in some cases
2.3.1 Only MLs (e.g. ANN)	simple using of raw acceleration data and filter
2.3.2 Combined MLs and feature extraction	- able to eliminate speed dependence, suspension variation - higher accuracy

As for future research, it should be of strong value-add to focus on several potential topics as follows. Firstly, the air-suspension system (as an active-suspension type) has not been investigated by any research for PR whereas most existing studies are about passive suspension system (67%). The reason is probably due to the high modelling complexity of the air-suspension while it is noted that the Macpherson controllable suspension was simulated and simplified in [2]. Secondly, MLs have demonstrated their capability for multiple functions such as ANN algorithms for PR and PD in [33], PR and RE in [8, 134] in which certain limitation in the estimation accuracy, vehicle parameters or speed variance can be further studied to develop comprehensive algorithms.

Thirdly, although fleet vehicle approach seems to be less complicated to deal with, a comprehensive PBPM for PD and RE for the fleet of public transport (e.g. bus fleet) is still missing, except the general concept in [64] or smart PD in [124]. This fleet vehicle approach faces fewer challenges on data aggregation since vehicle fleets are quite identical, with lower GPS errors caused by lane-by-lane difference and without crowdsourcing platform. Such a system, once developed, will be beneficial in maintaining the road condition for public transport such as the citywide bus lane system in Singapore, London or worldwide BRT lane system, in which the road surfaces often deteriorate quickly due to heavy-loading from heavy-duty vehicles [136]. Fourthly, how to localise precise road roughness condition and potholes by lane accuracy (probably less than 0.5 m accuracy) is crucial to make the PBPM comparable to conventional Pavement management system, in which APP instruments currently measure road surface lane-by-lane. Higher GPS localisation of potholes also serves to optimise the trajectories of following vehicles in the connected platooning to avoid road defects by passing the vibration information from the leader to the followers. This can be done with the help of the future development of sensor technology. Lastly, the intensive on-going research on RIF and TWIT [95‐101] as the alternatives for IRI in connected vehicle environment will be promising for large-scale implementation.

Acknowledgements

This work is part of the PhD study of the first author and is financially supported by the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme.

This work was supported by the German Research Foundation (DFG) and the Technical University of Munich (TUM) in the framework of the Open Access Publishing Program.

Competing interests

The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Titel: Response-based methods to measure road surface irregularity: a state-of-the-art review
verfasst von: Teron Nguyen
Bernhard Lechner
Yiik Diew Wong
Publikationsdatum: 01.12.2019
Verlag: Springer International Publishing
Erschienen in: European Transport Research Review / Ausgabe 1/2019
Print ISSN: 1867-0717
Elektronische ISSN: 1866-8887
DOI: https://doi.org/10.1186/s12544-019-0380-6

Springer Professional

Abstract

Purpose

Methods

Results

Publisher’s Note

1 Introduction

2 Literature data retrieval method

3 Results

3.1 Road profile reconstruction/estimation for vehicles dynamics control

3.1.1 Model-based methods (observers/estimators)

3.1.2 Data-driven methods/machine-learning techniques

3.1.3 Transfer functions and other techniques

3.1.4 Summary of methods for road profile reconstruction/estimation

3.2 Pothole detection and roughness index estimation

3.2.1 Threshold-based methods

3.2.2 Signal processing

3.2.3 Machine learning techniques

3.2.4 Summary of methods for pothole detection and road roughness estimation

4 Discussion, conclusion and outlook

Acknowledgements

Competing interests

Publisher’s Note

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