Evidence map›Paper›PMID 36366163›Full record

ArticleSensors (Basel, Switzerland)2022

An Intelligent Online Drunk Driving Detection System Based on Multi-Sensor Fusion Technology.

Juan Liu, Yang Luo, Liang Ge, Wen Zeng, Ziyang Rao, Xiaoting Xiao

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 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
1.4field-weighted citation impact, top 21% 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, 14 citations in OpenAlex.

  1. Review
  2. Article
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

6 authors at 2 institutions in 1 country.

Juan LiuSchool of Electric and Information, Southwest Petroleum University, Chengdu 610500, China.
Yang LuoSchool of Electric and Information, Southwest Petroleum University, Chengdu 610500, China.
Liang GeSchool of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China.ORCID 0000-0003-4549-6946
Wen ZengSchool of Materials Science and Engineering, Chongqing University, Chongqing 400044, China.ORCID 0000-0002-4136-5351
Ziyang RaoSchool of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China.
Xiaoting XiaoSchool of Electric and Information, Southwest Petroleum University, Chengdu 610500, China.
Southwest Petroleum University · CNChongqing University · CN

Funding

the International Science and Technology Cooperation Project of Chengdu 2020-GH02-00016-HZthe National Natural Science Foundation 51974273the Spe-cial Project of Science and Technology Strategic Cooperation between Nanchong City and Southwest Petroleum University SXHZ026/SXHZ038/ SXHZ024
6 · The paper itself

Abstract

Since drunk driving poses a significant threat to road traffic safety, there is an increasing demand for the performance and dependability of online drunk driving detection devices for automobiles. However, the majority of current detection devices only contain a single sensor, resulting in a low degree of detection accuracy, erroneous judgments, and car locking. In order to solve the problem, this study firstly designed a sensor array based on the gas diffusion model and the characteristics of a car steering wheel. Secondly, the data fusion algorithm is proposed according to the data characteristics of the sensor array on the steering wheel. The support matrix is used to improve the data consistency of the single sensor data, and then the adaptive weighted fusion algorithm is used for multiple sensors. Finally, in order to verify the reliability of the system, an online intelligent detection device for drunk driving based on multi-sensor fusion was developed, and three people using different combinations of drunk driving simulation experiments were conducted. According to the test results, a drunk person in the passenger seat will not cause the system to make a drunk driving determination. When more than 50 mL of alcohol is consumed and the driver is seated in the driver's seat, the online intelligent detection of drunk driving can accurately identify drunk driving, and the car will lock itself as soon as a real-time online voice prompt is heard. This study enhances and complements theories relating to data fusion for online automobile drunk driving detection, allowing for the online identification of drivers who have been drinking and the locking of their vehicles to prevent drunk driving. It provides technical support for enhancing the accuracy of online systems that detect drunk driving in automobiles.

Indexed as

Alcoholic IntoxicationAutomobile DrivingDriving Under the InfluenceAccidents, TrafficHumansOnline SystemsReproducibility of ResultsTechnologyalcohol ignition interlockdata fusiondrunk driving testintelligent detectionsensor array

Identifiers

PMID36366163
PMCPMC9653997
OpenAlexW4308332297

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.