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 research institutes in the project AIDE (Adaptive Integrated Driver-vehicle InterfacE). The project developed an interface with the driver that integrates different advanced driver assistant systems and in-vehicle information systems and adapted the interface to different driver or traffic conditions. This paper presents an overview of the AIDE project and will then focus on the adaptation aspect of AIDE. Information presented to the driver could be adapted on basis of environmental conditions (weather and traffic), and on basis of assessed workload, distraction, and physical condition of the driver. The adaptation of how information is presented to the driver or the timing of when information is presented to the driver is of importance. Adapting information, however, also results in systems that are less transparent to the driver.
KeywordsIn-car services workload adaptive user interface central management
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