  Using current sensing technology, a wealth of data on driving sessions is
potentially available through a combination of vehicle sensors and drivers'
physiology sensors (heart rate, breathing rate, skin temperature, etc.). Our
hypothesis is that it should be possible to exploit the combination of time
series produced by such multiple sensors during a driving session, in order to
(i) learn models of normal driving behaviour, and (ii) use such models to
detect important and potentially dangerous deviations from the norm in
real-time, and thus enable the generation of appropriate alerts. Crucially, we
believe that such models and interventions should and can be personalised and
tailor-made for each individual driver. As an initial step towards this goal,
in this paper we present techniques for assessing the impact of cognitive
distraction on drivers, based on simple time series analysis. We have tested
our method on a rich dataset of driving sessions, carried out in a professional
simulator, involving a panel of volunteer drivers. Each session included a
different type of cognitive distraction, and resulted in multiple time series
from a variety of on-board sensors as well as sensors worn by the driver.
Crucially, each driver also recorded an initial session with no distractions.
In our model, such initial session provides the baseline times series that make
it possible to quantitatively assess driver performance under distraction
conditions.
