Detection of driver sleepiness during daylight and darkness

Driving sleepiness is a serious problem worldwide. It is of interest to develop reliable sleepiness detection systems to implement in vehicles, and for such a system both physi-ological data and driver performance data can be used. The reasons for driver sleepiness can be many, where an interesting factor to consider is the light condition of the environment, specifically daylight and darkness. Daylight and darkness has shown to affect human sleepiness in general and it is therefore of importance to investigate the effect of it on driver sleepiness independent of other factors. This thesis aimed to investigate whether light condition is a parameter that should be considered when developing a sleepiness detection system in a vehicle. This was done by investigating if the course of sleepiness would be affected by daylight and darkness, and if adding light condition information as a parameter to a classification model improved the performance of the sleepiness classification. To achieve this, the study was based upon data collected from driving simulator tests conducted by the Swedish National Road and Transport Research Institute (VTI). Test subjects drove in simulated daylight and darkness during both daytime while rested and nighttime while sleep-deprived. An exploratory and statistical analysis was conducted of several sleepiness indicators extracted from physio-logical data and simulator data. Three different classification models were implemented. The indicators pointed to a higher level of driver sleepiness during night compared to during day, as well as an increase with time on task. However, no clear trends pointed to daylight and darkness having affected the sleepiness of the driver. The classification models showed a marginal improvement when including light condition as a feature, however not large enough to draw any specific conclusion regarding the effect. The conclusion was that an effect of daylight and darkness on the course of driver sleepiness could not be seen in this thesis. The adding of light and dark as a feature did not significantly improve the classification models’ performances. In summary, further investigations of the effect of daylight and darkness in relation to driver sleepiness are needed.