Ricerc@Sapienza

The car driving is considered a very complex activity, consisting of different tasks and subtasks. For such a reason, in particular situations the cognitive demand to the driver could be very high inducing a strong mental workload, and consequently a performance decreasing and an error probability increasing. To this regard, it has been demonstrated that human error is the main cause of the 57 % of road accidents and a contributing factor in over 90 % of them (Treat et al, 1979). Therefore, it becomes crucial to prevent drivers' performance decreasing in order to reduce the probability of error commission.
The car industry has focused its recent research activity on developing algorithms able to forecast aberrant driver's mental states, such as mental overload, fatigue, drowsiness, inattention, on the basis of changes in driver's performance and behaviour (head movements, steering control, frequency of actions on the steering wheel, etc.). However, in the last decades the neuroscientific and neuroergonomic research widely demonstrated how the neurophysiological measures are able to provide a human mental state evaluation more objective, more sensitive, and almost instantaneous, if compared with behavioural and performance measures (Scerbo et al., 2001). Also, thanks to the advances in the field of Brain-Computer Interface (BCI), the neurophysiological measures, and brain signals in particular, have been demonstrated to be the best candidate to trigger the operative systems (the so-called "Adaptive Automation" (AA) solutions), i.e. an automated interface that is able to adapt and reallocate its activities on the basis of the user's mental state (Aricò et al., 2016).
The DriveME project aims to validate an EEG-based mental workload index in real driving settings, in order to provide a tool for: (i) developing AA solutions triggered by neurometrics to support the drivers; (ii) investigating the effect of road infrastructure and car equipment on the driver's workload.