  Driving behaviour has a great impact on road safety. A popular way of
analysing driving behaviour is to move the focus to the manoeuvres as they give
useful information about the driver who is performing them. In this paper, we
investigate a new way of identifying manoeuvres from vehicle telematics data,
through motif detection in time-series. We implement a modified version of the
Extended Motif Discovery (EMD) algorithm, a classical variable-length motif
detection algorithm for time-series and we applied it to the UAH-DriveSet, a
publicly available naturalistic driving dataset. After a systematic exploration
of the extracted motifs, we were able to conclude that the EMD algorithm was
not only capable of extracting simple manoeuvres such as accelerations, brakes
and curves, but also more complex manoeuvres, such as lane changes and
overtaking manoeuvres, which validates motif discovery as a worthwhile line for
future research.
