  Driver distraction strongly contributes to crash-risk. Therefore, assistance
systems that warn the driver if her distraction poses a hazard to road safety,
promise a great safety benefit. Current approaches either seek to detect
critical situations using environmental sensors or estimate a driver's
attention state solely from her behavior. However, this neglects that driving
situation, driver deficiencies and compensation strategies altogether determine
the risk of an accident. This work proposes to use inverse suboptimal control
to predict these aspects in visually distracted lane keeping. In contrast to
other approaches, this allows a situation-dependent assessment of the risk
posed by distraction. Real traffic data of seven drivers are used for
evaluation of the predictive power of our approach. For comparison, a baseline
was built using established behavior models. In the evaluation our method
achieves a consistently lower prediction error over speed and track-topology
variations. Additionally, our approach generalizes better to driving speeds
unseen in training phase.
