Car driving temporal cognitive workload estimation using features of eye tracking

The mental workload of older motorists driving various routes on public roads in their own cars was evaluated using estimations of driver's attention levels based on saccade rates and pupillary changes. Levels of attention while driving were calculated using a Bayesian modelling technique. This was employed due to the extensive amount of data measured over the road course, which was lengthy. Mean attention levels of 5 categories of driving routes were summarised, and routes with turns required additional attention resources. Individual attention levels and confidence intervals correlate with some ratings of NASA-TLX factors, and with the factor for Frustration in particular.