Evidence map›Paper›PMID 36439736›Full record

ArticleHeliyon2022

Improvement of driver night vision in foggy environments by structured light projection.

Jaime Quintana Benito, Antonio A Fernández-Balbuena, Juan Carlos Martínez-Antón, Daniel Váquez Molini

Open access · goldAbstract read
In one paragraph

Article in Heliyon, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 51% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Jaime Quintana BenitoUniversidad Complutense de Madrid, Calle Arcos de Jalón, Madrid, 28037, Spain.
Antonio A Fernández-BalbuenaUniversidad Complutense de Madrid, Calle Arcos de Jalón, Madrid, 28037, Spain.
Juan Carlos Martínez-AntónUniversidad Complutense de Madrid, Calle Arcos de Jalón, Madrid, 28037, Spain.
Daniel Váquez MoliniUniversidad Complutense de Madrid, Calle Arcos de Jalón, Madrid, 28037, Spain.
Universidad Complutense de Madrid · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nowadays, fog is still a natural phenomenon that hinders our ability to detect targets, particularly in the field of driving where accidents are increasing. In the literature we find different studies determining the range of visibility, improving the quality of an image, determining the characteristics of fog, etc. In this work we propose the possibility of using a structured lighting system, on which we project the light towards the target, limiting the field lighting. We have developed a scattering light propagation model to simulate and subsequently study the veil luminance, generated by backscattering, to predict the decrease in visibility. This simulation considers the type of fog, the relative orientation of various elements (observer, light source and targets). We have built a fog chamber to validate the experimental params of the described system. The results obtained from both the simulation and the experimental measurements demonstrate that it is possible to obtain a high contrast enhancement for viewing a target when illuminated as described. Clearly, this kind of lighting technology will improve the road safety in foggy night environments. The results of this work can also be extrapolated to any situation in which the visibility of an observer is compromised by the environment, such as heavy rain, smoke from fires, among others.

Indexed as

Fog and light scatteringFog chamberFog visibility sceneSimulated fog sceneSmart lightingVeiling luminance

Identifiers

PMID36439736
PMCPMC9694105
OpenAlexW4310249182

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.