Ellipsoid-based approximation method for the estimation of the actual reduced luminance coefficients of road surfaces for accurate lighting simulations

Ogando-Martínez, Ana; Troncoso-Pastoriza, Francisco; Granada, Enrique; Eguía, P.

doi:10.1016/j.scs.2020.102502

Cited by 8 publications

(6 citation statements)

References 38 publications

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“…The r -tables can also be seen as BRDFs reduced at two dimensions. A few studies were interested in r -table modelling 27,32,33 but many dealt with BRDF models. 46 Suitable analytic BRDF models are not often available and a common solution is to choose a suitable function space to approximate measured BRDFs in that space.…”

Section: R-tables Basis Functionsmentioning

confidence: 99%

“…[20][21][22][23][25][26][27] Thanks to recent imaging luminance measurement devices (ILMD) 26,[28][29][30][31] and lighting software, 3,4 it is also possible to compare simulated versus measured photometric parameters and luminance maps with a feedback loop to aim for the right model. 32,33 An original method has also been carried out by Buschmann et al 34 using a dedicated on-site illumination set-up. Thanks to a calibrated luminaire and luminance image processing, some r-table elements are calculated, and used to simulate luminance maps.…”

Section: Introductionmentioning

confidence: 99%

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Space of basis functions to retrieve road surface reflection tables from luminance maps

Boucher

Greffier

2022

Lighting Research & Technology

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Road surface reflection tables ( r-tables) relate scene illuminance to luminance seen by a car driver. They are important for many road lighting tasks accounting for road optical properties, such as new illumination design, new pavement texture or lighting design software, to reduce energy consumption without losses on safety and visibility. This paper aims first at finding a space of basis functions to describe r-tables. From a database of 34 r-tables covering a large variety of pavements, a principal component analysis allows to construct a 33-dimensional space, basis for r-table representations. From that statistical model, a method is exposed to retrieve r-table from a luminance map. The estimated r-table is then used to calculate a reconstructed luminance map. Road lighting quality criteria are also derived and they demonstrate the relevancy of the estimated r-table. Finally the model is tested with noisy input data and it remains stable and reliable, making it applicable with experimental luminance maps.

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Section: R-tables Basis Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Space of basis functions to retrieve road surface reflection tables from luminance maps

Boucher

Greffier

2022

Lighting Research & Technology

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“…However, the standard r-tables obtained from measurement data in the 1970s do not widely represent the reflectance characteristics of roads today, and the use of uncorrected r-tables for lighting design will lead to a deviation between the designed and actual brightness of the pavement [6]. In recent years, increasing research efforts have been devoted to obtaining reduced-brightness coefficient tables [7][8][9][10][11] or developing different devices [12][13][14] to accu-rately obtain the optical reflectance characteristics of actual roads and improve the accuracy of road-lighting design.…”

Section: Introductionmentioning

confidence: 99%

Influence of the Surface Texture Parameters of Asphalt Pavement on Light Reflection Characteristics

Xu,

Qian,

Zhang

et al. 2023

Applied Sciences

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The optical reflection characteristics of asphalt pavement have an important influence on road-lighting design, and the macrotexture and microtexture of asphalt pavement significantly affect its reflection characteristics. To investigate the impact of texture parameters on the retroreflection coefficient of asphalt pavement, the texture indices of rutted plate specimens and field asphalt pavement were obtained by a pavement texture tester, including the macrotexture surface area (S1), microtexture surface area (S2), macrotexture distribution density (D1), microtexture distribution density (D2), root mean square slope (Δq), skewness (Rsk), and steepness (Rku). The corresponding retroreflective coefficient RL was measured by using a retroreflectometer. In the laboratory experiments, rutted specimens of AC-13, SMA-13, and OGFC-13 asphalt mixtures were formed. The changes in texture parameters and the retroreflection coefficient of rutting specimens before and after rolling were studied, and a factor-influence model between macro- and microtexture parameters and RL was established, along with correlation models of the texture index and RL. The results show that after the rutting test, S1, S2, D1, D2, Δq, and Rku decreased, Rsk increased, and RL increased. In the single-factor model, the parameters could be used to characterize RL with high prediction accuracy, whereas for the onsite measurements, the parameters Δq, Rsk, and Rku could well characterize RL. The nonlinear model established, based on the BP neural network algorithm, improved the prediction accuracy. This research provides ideas for optimizing the reflection characteristics of asphalt pavement and a decision-making basis for road-lighting design.

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“…Worldwide, electric lighting accounts for 15% of the electricity consumption and 5% of greenhouse gas emission. 1,2 Improving the energy efficiency of lighting therefore has great potential for reducing energy consumption and achieving carbon neutrality. 3,4 Road lighting supports visual safety, security and comfort, which is one of a significant types among electric lightings and also an indispensable service for cities and divers.…”

Section: Introductionmentioning

confidence: 99%

Exploring the effect of pavement reflection and photometric properties on road lighting performance

Xie

Harvey

et al. 2022

Lighting Research & Technology

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The reflection properties of pavement have an impact on the lit environment and thus upon drivers’ vision and comfort and the energy consumption of the lighting installation. The reflection properties combine diffuse and specular components. The specular reflection component changes with different materials: it also changes when the surface is wet, although this is sometimes ignored in lighting design. This study used simulation (DIALux 4.13) to investigate the effect on lighting parameters (luminance, overall uniformity, longitudinal uniformity and threshold increment (TI)) of changes in pavement reflection properties using different pavement materials and under dry and wet conditions. Furthermore, comparison of lighting parameters was made with different road lighting arrangements. The results show that an increase in the specular reflection component leads to an increase in luminance and a decrease in uniformity. Of the surfaces investigated, the porous pavement had the lower luminance but better uniformity. Arranging the lighting installation based on the extreme wettest condition could make luminance and uniformity rise but with an increase of 2–2.5 kWh/m2 annual energy consumption. When trying to control glare problems during design process, it is suggested that uniformity cannot be neglected except the TI.

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Ellipsoid-based approximation method for the estimation of the actual reduced luminance coefficients of road surfaces for accurate lighting simulations

Cited by 8 publications

References 38 publications

Space of basis functions to retrieve road surface reflection tables from luminance maps

Space of basis functions to retrieve road surface reflection tables from luminance maps

Influence of the Surface Texture Parameters of Asphalt Pavement on Light Reflection Characteristics

Exploring the effect of pavement reflection and photometric properties on road lighting performance

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