Adaptive interfaces in driving

The automotive domain is an excellent domain for investigating augmented cognition methods, and one of the domains that can provide the applications. We developed, applied and tested indirect (or derived) measures to estimate driver state risks, validated by direct state-sensing methods, with major European vehicle manufacturers, suppliers and...

Designing automated handheld navigation support

Map usage on handheld devices suffers from limited screen size and the minimal attention that users can dedicate to them in mobile situations. This work examines effects of automating navigation features like zooming and panning as well as other features such as rotation, path finding and artifact representation on the mobile navigation...

Attuning in-car user interfaces to the momentary cognitive load

Cars, trucks and busses are more and more equipped with functions and services that drivers are supposed to operate and understand. The most important developments in this area are the Advanced Driver Assistance Systems (ADAS) and In Vehicle Information Systems (IVIS). In order to make sure that the driver understands and appreciates (comfort)...

Adaptive in-car user interfaces based on personalized workload estimation

Based on recent research on workload scheduling and personalization, we developed a personal electronic driver assistant that mediates the interactions between the driver and in-car services in order to prevent overload. Whereas other approaches for overload prevention often focus on specific workload sources (e.g. phone calls), our assistant...

Usability trade-offs for adaptive user interfaces: Ease of use and learnability

An analysis of context-aware user interfaces shows that adaptation mechanisms have a cost-benefit trade-off for usability. Unpredictable autonomous interface adaptations can easily reduce a system's usability. To reduce this negative effect of adaptive behaviour, we have attempted to help users building adequate mental models of such systems. A...