Information Management for Adaptive Automotive Human Machine Interfaces

Abstract

Adaptive Human Machine Interfaces (HMI) provide a substantial contribution to avoid information overload of the driver. This paper presents novel concepts for context-aware information management in the sector of adaptive automotive HMI. In particular, we describe an architectural design which supports an easy integration of multiple strategies to manipulate and to adapt the in-vehicle information flow depending on the current driving context. The presented architecture follows the blackboard design pattern and enables opportunistic reasoning in the automotive domain. We further present a novel fusion strategy for messages based on a taxonomic message model. The fusion strategy allows to combine two or more low-level messages and to replace them with a higher-level message in order to reduce the information load for the driver. The described concepts have been evaluated with the help of a driving simulator study with N = 41 test persons. The results show a significant reduction of the subjective workload if the information manager is applied.