  Semi-autonomous driving, as it is already available today and will eventually
become even more accessible, implies the need for driver and automation system
to reliably work together in order to ensure safe driving. A particular
challenge in this endeavour are situations in which the vehicle's automation is
no longer able to drive and is thus requesting the human to take over. In these
situations the driver has to quickly build awareness for the traffic situation
to be able to take over control and safely drive the car. Within this context
we present a software and hardware framework to asses how aware the driver is
about the situation and to provide human-centred assistance to help in building
situation awareness. The framework is developed as a modular system within the
Robot Operating System (ROS) with modules for sensing the environment and the
driver state, modelling the driver's situation awareness, and for guiding the
driver's attention using specialized Human Machine Interfaces (HMIs).
  A particular focus of this paper is on an Answer Set Programming (ASP) based
approach for modelling and reasoning about the driver's interpretation and
projection of the scene. This is based on scene data, as well as eye-tracking
data reflecting the scene elements observed by the driver. We present the
overall application and discuss the role of semantic reasoning and modelling
cognitive functions based on logic programming in such applications.
Furthermore we present the ASP approach for interpretation and projection of
the driver's situation awareness and its integration within the overall system
in the context of a real-world use-case in simulated as well as in real
driving.
