The driver's ability to understand a current traffic situation, and anticipate future maneuvers of other traffic participants are key safety enablers. How can we help drivers observe the traffic safety of different traffic situations via information and warnings via a safety system?

Cognitive predictive interaction with driver monitoring and Advanced Driver Assistance Systems (ADAS) can potentially enhance situational awareness by flexibly providing different drivers with information that are critical for safe driving e.g. in traffic intersections with other road users. The development of this interaction needs further research and applications to contribute to traffic safety.

Our project will analyze and evaluate effective differences in cognitive factors in the interaction between different traffic situations, different drivers (age and gender), and information from the driver monitoring system and different ways of interaction with the ADAS.

Current driving experimental simulations that test driver monitoring systems and ADAS will be used to further determine the cognitive predictive limitations that need to be further supported to increase more reliable interaction with the ADAS and hence increases traffic safety. Traffic situations, time frames, driver characteristics (e.g., age, gender, mood, driving style), reaction time, and accuracy (crash avoidance) are variables that will be used.

The goal is to identify the key cognitive prediction factors for interaction with ADAS and based on that to also propose the further development of driver monitoring and driver support functions that address these key factors in order to create a higher traffic safety by designing more individually adaptable driver support systems, that play to the individual drivers strength and help them overcome their weaknesses.