Online prediction of driver distraction based on brain activity patterns

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Engineering

ONLINE PREDICTION OF DRIVER DISTRACTION
BASED ON BRAIN ACTIVITY PATTERNS
ABSTRACT
This paper presents a new computational framework for early detection of driver
distractions (map viewing) using brain activity measured by electroencephalographic (EEG)
signals. Compared with most studies in the literature, which are mainly focused on the
classification of distracted and nondistracted periods, this study proposes a new framework to
prospectively predict the start and end of a distraction period, defined by map viewing. The
proposed prediction algorithm was tested on a data set of continuous EEG signals recorded from
24 subjects. During the EEG recordings, the subjects were asked to drive from an initial position
to a destination using a city map in a simulated driving environment. The overall accuracy values
for the prediction of the start and the end of map viewing were 81% and 70%, respectively. The
experimental results demonstrated that the proposed algorithm can predict the start and end of
map viewing with relatively high accuracy and can be generalized to individual subjects. The
outcome of this study has a high potential to improve the design of future intelligent navigation
systems. Prediction of the start of map viewing can be used to provide route information based
on a driver’s needs and consequently avoid map-viewing activities. Prediction of the end of map
viewing can be used to provide warnings for potential long map-viewing durations. Further
development of the proposed framework and its applications in driver-distraction predictions are
also discussed.

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