Estimation of road profile variability from measured vehicle responses

Fauriat, William; Mattrand, Cécile; Gayton, Nicolas; Béakou, A.; Cembrzynski, T.

doi:10.1080/00423114.2016.1145243

Cited by 65 publications

(54 citation statements)

References 16 publications

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“…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.…”

Section: Model-based Methods (Observers/estimators)mentioning

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: Model-based Methods (Observers/estimators)mentioning

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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“…One of the most used techniques nowadays is the Kalman filtering. This method was modified by Fauriat et al (2016) who used the "Augmented Kalman filter" and showed that this technique gave a good estimation result. But it still has some limitations.…”

Section: Introductionmentioning

confidence: 99%

“…The aim of this paper is to investigate the efficiency of applying the ICA technique in identification of external excitations applied to vehicles (Zarzoso and Comon, 2010;Nakamura et al, 2012;Ben Hassen et al, 2017a,c,d). The results of estimation were validated through comparing the obtained results with those of the Kalman filtering developed by Fauriat et al (2016).…”

Section: Introductionmentioning

confidence: 99%

Road profile identification using estimation techniques: comparison between independent component analysis and Kalman filter

Chaabane¹,

Hassen

Abbès³

et al. 2019

Journal of Theoretical and Applied Mechanics

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This paper focuses on the identification of a road profile disturbance acting on vehicles. Vehicles are subjected to many kinds of excitation sources such as road profile irregularities, which constitute a major area of interest when designing suspension systems. Indeed, determining the road profile is important for passive suspension design on the one hand and for determining an appropriate control law for active suspensions on the other. Direct measurement techniques of the road profile are expensive, so solutions based on estimation theory are needed. The aim of this paper is to characterize the road excitation using the Independent Component Analysis (ICA). This proposed method can reconstruct original excitation sources by using physically measurable signals of the system under study. Here, the estimation of road disturbances is considered as output sources and identified from dynamic responses of the vehicle. These responses can be measured via sensors or can be numerically computed. In our case, they are numerically simulated using the Newmark method and consider different types of road profiles. The obtained results are validated after using a comparison with the Kalman filtering. The robustness of the ICA is confirmed via parametric study.

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“…However, this study did not take into account the effects of cornering. Further to this, a data processing algorithm was proposed to estimate road profiles from the dynamic responses measured on the vehicle and validated using experimental data obtained from simulations and real measurements [13]. The proposed method could be applied to extract valuable statistical information on road roughness.…”

Section: Introductionmentioning

confidence: 99%

“…A real-time estimation method was developed to estimate the road profile based on a KF and experimental results showed high accuracy [6]. Moreover, dynamic responses were used to estimate the road profiles based on KF in a stochastic framework and validated using experimental data obtained from simulations and real measurements [13].…”

Section: Introductionmentioning

confidence: 99%

Road profile estimation for suspension system based on the minimum model error criterion combined with a Kalman filter

Wang¹,

Dong²,

Qin³

et al. 2017

J. vibroeng.

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This paper presents a novel approach for improving the estimation accuracy of the road profile for a vehicle suspension system. To meet the requirements of road profile estimation for road management and reproduction of system excitation, previous studies can be divided into data-driven and model based approaches. These studies mainly focused on road profile estimation while seldom considering the uncertainty of parameters. However, uncertainty is unavoidable for various aspects of suspension system, e.g., varying sprung mass, damper and tire nonlinear performance. In this study, to improve the estimation accuracy for a varying sprung mass, a novel algorithm was derived based on the Minimum Model Error (MME) criterion and a Kalman Filter (KF). Since the MME criterion method utilizes the minimum value principle to solve the model error based on a model error function, the MME criterion can effectively deal with the estimation error. Then, the proposed algorithm was applied to a 2 degree-of-freedom (DOF) suspension system model under ISO Level-B, ISO Level-C and ISO Level-D road excitations. Simulation results and experimental data obtained using a quarter-vehicle test rig revealed that the proposed approach achieves higher road estimation accuracy compared to traditional KF methods.

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Estimation of road profile variability from measured vehicle responses

Cited by 65 publications

References 16 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

Road profile identification using estimation techniques: comparison between independent component analysis and Kalman filter

Road profile estimation for suspension system based on the minimum model error criterion combined with a Kalman filter

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