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Table 7 Advantages and disadvantages of response-based methods

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

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