Objective: Drowsiness is a major cause of driver impairment leading to crashes and fatalities. Research has established the ability to detect drowsiness with various kinds of sensors. We studied drowsy driving in a high-fidelity driving simulator and evaluated the ability of an automotive production-ready driver monitoring system (DMS) to detect drowsy driving. Additionally, this feature was compared to and combined with signals from vehicle-based sensors. Methods: The National Advanced Driving Simulator was used to expose drivers to long, monotonous drives. Twenty participants drove for about 4 h in the simulator between 10 p.m. and 2 a.m. They were allowed to use cruise control and traffic was sparse and semirandom, with both slowerand faster-moving vehicles. Observational ratings of drowsiness (ORDs) were used as the ground truth for drowsiness, and several dependent measures were calculated from vehicle and DMS signals. Drowsiness classification models were created that used only vehicle signals, only driver monitoring signals, and a combination of the 2 sources. Results: The model that used DMS signals performed better than the one that used only vehicle signals; however, the combination of the two performed the best. The models were effective at discriminating low levels of drowsiness from moderate to severe drowsiness; however, they were not effective at telling the difference between moderate and severe levels. A binary model that lumped drowsiness into 2 classes had an area under the receiver operating characteristic (ROC) curve of 0.897. Conclusions: Blinks and saccades have been shown to be predictive of microsleeps; however, it may be that detection of microsleeps and lane departures occurs too late. Therefore, it is encouraging that the model was able to distinguish mild from moderate drowsy driving. The use of automation may make vehicle-based signals useless for characterizing driver states, providing further motivation for a DMS. Future improvements in impairment detection systems may be expected through a combination of improved hardware, physiological measures from unobtrusive sensors and wearables, and the intelligent integration of environmental variables like time of day and time on task.
Driver monitoring systems are growing in importance as well as capability. This paper reports on drowsy driving detection models generated from multiple sources of driver monitoring data. Behavioral (driver) data were provided by a camera-based production-type driver monitoring system manufactured by Aisin Technical Center of America (from the Aisin Group). Vehicular data were recorded from the National Advanced Driving Simulator’s large-excursion motion-base driving simulator. Forty participants drove the simulator for up to 3 h after being awake for at least 16 h. Periodic measurements of drowsiness were made every 10 min using both observational ratings of drowsiness by an external rater and the self-reported Karolinska Sleepiness Scale. A novel application of sequence analysis with clustering and hidden Markov models resulted in models that tracked well with the subjective drowsiness measures. The area under Receiver Operating Characteristic curves evaluating the models ranged from 0.85 to 0.87. By allowing for many distinct patterns observed in driving sequences, the hope is that the method will offer a robust way to accommodate pattern variability that naturally occurs over time and among drivers.
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