This deliverable provides better understanding of whether and how drivers manage their secondary task activities. An automated procedure has been applied to provide candidate cases of secondary tasks to manual annotators. The automatic annotation tool is based on deep learning algorithms. The focus of the research questions was on self-regulation, on how drivers manage their secondary task activity in the context of the dynamics of the traffic and road situation. That man-agement includes the determination not to engage in such tasks in the first place or only to engage in some particular activities. The deliverable finds that car drivers spent 10.2% of their driving time engaged in some kind of secondary tasks. The total time spent in all the secondary tasks for truck drivers sums up to about 20%. The duration of secondary task was affected by complexity of manoeuvre. There are thus indications of some self-regulation by drivers. (Author/publisher)
Abstract