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).
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Accuracy of the DRIVE-model: Diagnostic accuracy of the drunk driving warning system (DRIVE) to detect drunk driving (>= 0.25 mg/l breath alcohol concentration (BrAC)) quantified as the area under the receiver operator characteristics curve (AUC ROC).
Timeframe: 480 minutes