  Driver support systems that include human states in the support process is an
active research field. Many recent approaches allow, for example, to sense the
driver's drowsiness or awareness of the driving situation. However, so far,
this rich information has not been utilized much for improving the
effectiveness of support systems. In this paper, we therefore propose a warning
system that uses human states in the form of driver errors and can warn users
in some cases of upcoming risks several seconds earlier than the state of the
art systems not considering human factors. The system consists of a behavior
planner Risk Maps which directly changes its prediction of the surrounding
driving situation based on the sensed driver errors. By checking if this
driver's behavior plan is objectively safe, a more robust and foresighted
driver warning is achieved. In different simulations of a dynamic lane change
and intersection scenarios, we show how the driver's behavior plan can become
unsafe, given the estimate of driver errors, and experimentally validate the
advantages of considering human factors.
