Analysis of Road Surface Profiles

Bruscella, Ben; Rouillard, Vincent; Sek, Michael A

doi:10.1061/(asce)0733-947x(1999)125:1(55)

Cited by 64 publications

(47 citation statements)

References 4 publications

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“…This approach was further developed in (Bruscella et al, 1999;Rouillard, 2004Rouillard, , 2009). The variability of variances was modelled by a discrete distribution taking a few values.…”

Section: Road Roughness Modellingmentioning

confidence: 99%

“…Knowing durations and sizes of standard deviations the model is a non-stationary Gaussian process. Similar approaches were used in (Bruscella et al, 1999), and (Rouillard, 2004(Rouillard, , 2009. The variability of the standard deviation was modelled by a discrete distribution taking a few number of values.…”

Section: Laplace Modelling Of Road Profilesmentioning

confidence: 99%

“…Several approaches has been suggested to extend Gaussian modelling in order to capture this particular behaviour, see e.g. (Bogsjö, 2007b;Charles, 1993;Bruscella et al, 1999;Rouillard, 2004Rouillard, , 2009) and the references therein. The road topography modelling is closely related to simulation of vehicle missions and definition of stochastic drive cycle profiles for evaluation of fuel consumption, see e.g.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Laplace distribution models for road topography and roughness

Johannesson¹,

Podgórski

Rychlik

2017

IJVP

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“…This approach was further developed in (Bruscella et al, 1999;Rouillard, 2004Rouillard, , 2009). The variability of variances was modelled by a discrete distribution taking a few values.…”

Section: Road Roughness Modellingmentioning

confidence: 99%

Section: Laplace Modelling Of Road Profilesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Laplace distribution models for road topography and roughness

Johannesson¹,

Podgórski

Rychlik

2017

IJVP

View full text Add to dashboard Cite

“…This interaction results in a normal force which depends on the road surface profile as well as vehicle properties such as the characteristics of the suspension and the vehicle weight. The road surface profile is of interest in different applications including riding comfort assessment or road maintenance [33]- [35]. It is commonly modeled as a random process and characterized by a power spectral density (PSD) such as specified by the ISO 8608 standard [36].…”

Section: B Accelerometer Sensor Modelmentioning

confidence: 99%

Joint Vehicle Trajectory and Model Parameter Estimation Using Road Side Sensors

2015

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Abstract-This article shows how a particle smoother based system identification method can be applied for estimating the trajectory of road vehicles. As sensors, a combination of an accelerometer measuring the road surface vibrations and a magnetometer measuring magnetic disturbances mounted on the side of the road are considered. First, sensor models describing the measurements of the two sensors are introduced. It is shown that these depend on unknown, static parameters that have to be considered in the estimation. Second, the sensor models are combined with a two-dimensional constant velocity motion model. Third, the system identification algorithm is introduced which iteratively runs a Rao-Blackwellized particle smoother to estimate the vehicle trajectory followed by an expectation-maximization step to estimate the parameters. Finally, the method is applied to both simulation and measurement data. It is found that the method works well in general and some issues when real data is considered are identified as future work.

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“…This approach was further developed in [5,15]. The variability of variances was modeled by a discrete distribution taking a few values.…”

Section: Introductionmentioning

confidence: 99%