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Design and Implementation of a Drunk Driving Detection System — DRIVE  

Alcohol Drinking Clinical Trial

Official title:
Non-randomised, Controlled, Interventional Single-centre Study for the Design and Evaluation of an In-vehicle Drunk Driving Detection System

Clinical Trial Summary

To analyse driving behavior of individuals under the influence of alcohol using a validated research driving simulator. Based on the driving variables provided by the simulator the investigators aim at establishing algorithms capable of discriminating sober and drunk driving patterns using machine learning neural networks (deep machine learning classifiers).

Clinical Trial Description

Driving under the influence of alcohol (or "drunk driving") is one of the most significant causes of traffic accidents. Alcohol consumption impairs neurocognitive and psychomotor function and has been shown to be associated with an increased risk of driving accidents. Automotive technology is highly dynamic, and fully autonomous driving might, in the end, resolve the issue of alcohol impaired accidents. However, autonomous driving (level 4 or 5) is likely to be broadly available only to a substantially later time point than previously thought due to increasing concerns of safety associated with this technology. Therefore, solutions bridging the upcoming period by more rapidly and directly addressing the problem of drunk driving-associated traffic incidents are urgently needed. On the supposition that driving behaviour differs significantly between sober and drunk states, the investigators assume that different driving patterns in both states can be used to generate drunk driving detection models using machine learning neural networks (deep machine learning classifiers). ;

Study Design

Related Conditions & MeSH terms

Clinical Trial Details
NCT number NCT04980846
Study type Interventional
Source University of Bern
Contact
Status Completed
Phase N/A
Start date August 15, 2021
Completion date November 14, 2021
View Details
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