Takeover in Conditional Automation Driving
The car industry currently stands before a technical revolution. It is the goal of many car manufacturers, to implement conditional (Level 3) and high automation (Level 4) within the next decade. Secondary tasks, e.g. using In-Vehicle Infotainment (IVI) systems or mobile phones, depict a great challenge for driving safety, since they distract the driver's attention from the original driving task. Specifically, in scenarios in which autonomous driving is enabled and requires a shortterm driver reaction, the human-machine-interaction has to be structured ideally. In doing so, additional demands are shed on the available cognitive resources of the driver.
A potential solution in dealing with such demands consists in supporting drivers with an appropriate design of the environment and used interface. For this purpose, the respective level of driver workload and attention has to be measured as exactly as possible. On methodological accounts, besides of behavior-related performance measures (e.g. reaction times) or physiological indicators (e.g. gaze movement, EEG), inspecting human information processing by using cognitive models might be a valuable approach to shed light on this issues.
In cooperation with the Volkswagen AG , corporate research on driver modeling and evaluation, we developed cognitive models on driver workload in applied settings within the cognitive architecture ACT-R.
Currently, we are developing cognitive models on driver workload and attention in applied settings in cooperation with Daimler Trucks .
Team Takeover in Conditional Automation Driving
Nele Russwinkel 
Alexander Lotz 
- M.Sc. Hannah Kosanke: Coordinating Tasks in a Simplified Driving Environment Modelled with ACT-R and Threaded Cognition  (2016)
- M.Sc. Marika Nürnberg: Experimentelle Erfassung von Workload im Kontext der Fahrerbeanspruchung  (2015)
- Dr.-Ing. Hardy Smieszek: Macrocognitive Modeling of Mental Workload of Air Traffic Controlers with Coloured Petri Nets  (24.10.2014)