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Driving performance during a 1 1/2 h driving session was rated both by the drivers and their instructors.
Both information is used to obtain feature vectors which represent a driving session.
During each driving session we logged the steering wheel- and lane position signals at 60 Hz.
We replicated our findings across eight curved road segments used for validation in each driving session.
The code section is then completely executed in order to modify the driving session conditions if necessary.
One important parameter that effects the performance of the proposed method is the duration of the driving session.
In Fig. 10, histogram modelling of lane deviation and collision time values of an aggressive and a smooth driving session is presented.
For the characterization of the driving session, four different features are chosen considering the aggressive driving indicating behaviours as explained in the "Proposed method" section.
Moreover, obtaining the optical flow directly during the driving session requires the analysis of medium- or high-resolution video sequences in real time which requires significant computing capacity.
During each driving session the subjects self-reported their sleepiness level on the 9-grade Karolinska Sleepiness Scale (KSS) every five minutes [24].
All of these data can be used to replay driving sessions in the laboratory and to extract data associated with different driving session segments.
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