To prevent road crashes it is important to understand driver related contributing factors, which have been suggested to be the critical reason in 94 per cent of crashes. The overall aim of the project Vehicle Driver Monitoring has been to advance the understanding of two such factors; sleepiness and cognitive distraction. The project aimed to find methods to measure these two states, with focus on physiological measures, and to study their effect on driver behaviour. Other important questions concerned effects of environmental, inter- and intra-individual factors and if it is possible to detect driver sleepiness and cognitive distraction using machine learning methods. It is generally believed that sleepiness is easy to measure, but the quantification of sleepiness remains a challenge and a solid physiological measure of sleepiness is yet to be found. Sleepiness and sleep are active dynamic processes, which sometimes only affect local parts of the brain. Taking these temporal and spatial dynamics into account opens up for new ways to measure driver sleepiness, and also helps to gain a deeper understanding of its effects on driver performance. The effects of cognitive distraction (such as cell phone conversations) on traffic safety are not clear, as different studies reach different conclusions. The recently formulated cognitive control hypothesis suggests that “cognitive load selectively impairs driving subtasks that rely on cognitive control but leaves automatic performance unaffected”. The hypothesis can help to understand the role of cognitive distraction in crash causation. A key difficulty in research on cognitive distraction is that validated ways of measuring it during driving are lacking. Brain activity measures are attractive candidates because of their high face validity. However, since brain activity is difficult to record in real driving, as well as hard to interpret in general, other measures are also relevant to explore. The data collection was done in laboratory and driving simulator experiments. One sleepiness simulator experiment was performed. It was unique in its design with participants repeating their drives on six occasions, three times during daytime and three times during night-time. Two cognitive distraction simulator experiments were designed to advance the understanding of effects of cognitive distraction in both non-critical and critical driving scenarios. Drivers’ physiological and behavioural responses to sleepiness, cognitive distraction and certain contextual factors were studied. Key results were: * There was a relationship between lane departures and local sleep in brain regions associated with motor function. * Self-reported sleepiness level and driver performance differed within an individual when the same experiment was repeated three times in identical settings. * Darkness was found to be an additive factor in several sleepiness indicators but had no effect on the number of line crossings. * Professional drivers reported lower levels of sleepiness, even though the more objective indicators indicated that they were actually sleepier than the non-professional drivers. * Support for the Cognitive Control Hypothesis was found in different traffic scenarios. * The pupil diameter was the physiological measure with the closest relationship to cognitive load. * It was demonstrated that while several physiological measures correlated with the level of cognitive load, their similarities and differences at the same time reflected other driver state variations. * Well established EEG frequency power measures only showed a difference between levels of cognitive load when the driving task was simple. * A novel combined approach showed better results in mobile EEG artefact handling compared to available state of the art algorithms. * Automatic sleepiness and cognitive load classifications were improved by the use of contextual and behavioural measures as compared to physiological measures only. Taken together, the results clearly demonstrate that context (including both individual and environmental factors) has a great impact on driver behaviours, measures and experiences. From an overall perspective, further research is needed to increase the understanding of the contextual effects and to learn how they can be compensated for. Further research should also continue to focus on how cognitive load and sleepiness affects traffic safety. For example, by continued research on the effects of local sleep, and the dynamic interplay between the driver’s state and the driving task, especially in traffic situations where cognitive control is needed. In addition there is a need to investigate how indicators are influenced by multiple concurrent factors like cognitive load and sleepiness. (Author/publisher)
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