Evaluation of Driver Performance in Semi-automated Driving by Physiologic, Driver Behaviour and Video-based Sensors
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Beschreibung
Driving monitoring is one of the main requirements of the upcoming automated driving systems because drivers must still be alert to take over the vehicle control in case of system malfunction. Therefore, this study aimed to design an accurate, predictive and robust driver drowsiness classification system.
Drowsy driving is an important cause of road accidents that can lead to many fatalities and monetary losses. Moreover, in the upcoming SAE level 3 (conditionally automated driving), the state of the drivers must be monitored since the driver must be attentive to drive manually when the automated driving system cannot control the car any more. To detect driver drowsiness, three data sources have been generally used in the literature: vehicle-based data, facial-based data, and biosignals. Recent studies mostly focused on designing driver drowsiness detection systems using binary classifiers that report the driver’s vigilance into two classes, alert and drowsy. However, adding a middle level of drowsiness can help better estimate the transition between alertness and drowsiness to warn the driver early enough to prevent impaired driving. In addition, previous works mainly concentrated on driver drowsiness detection in manual driving mode, whereas there is no input from the driver in SAE level 3 automated driving. Therefore, the drowsiness detection system cannot utilize vehicle-based data to estimate the drowsiness in automated driving. To address the issues of the previous works, this thesis proposes three new approaches to classify driver drowsiness in simulated driving tests.
Autor*in
Sadegh Arefnezhad
Themen in »Evaluation of Driver Performance in Semi-automated Driving by Physiologic, Driver Behaviour and Video-based Sensors«
Drowsiness Driving Safety Driver Monitoring Deep learning ECG EEG