An integrated method for Adaptive automotive Human Machine Interfaces

Abstract

Adaptive Human Machine Interfaces (HMI) provide a substantial contribution to avoid an information overload of the driver by adapting the information presentation in accordance to the current driving situation. Although a well-defined model serves as the base of any context-aware system, current approaches for adaptive HMIs take into account only a limited model of the driver, the vehicle and the environmental aspects. This paper presents a feasible approach to model, interpret and classify driving situations. We propose a taxonomy for modelling relevant situational information. An event-based interpretation mechanism allows a deduction of spatio-temporal relations. Based on the situation model, the current situation is identified by performing probabilistic inference. The proposed method serves as a base for a context-aware information management and is demonstrated with help of an example scenario.