Behaviour adaptation using interaction patterns with augmented reality elements

Abstract

This publication describes a systematic approach for behaviour adaptations of humans, based on interaction patterns as a fundamental way to design and describe human machine interaction, and on image schemas as the basic elements of the resulting interaction. The natural learning path since childhood involves getting knowledge by experience; it is during this process that image schemas are built. The approach described in this paper was developed in close interplay with the concepts of cooperative guidance and control (CGC), where a cooperative automation and a human control a machine together, and of augmented reality (AR), where a natural representation of the world, e.g. in form of a video stream, is enriched with dynamic symbology. The concept was instantiated as interaction patterns “longitudinal and lateral collision avoidance”, implemented in a fix based simulator, and tested with professional operators whether driving performance and safety in a vehicle with restricted vision could be improved. Furthermore, it was tested whether interaction patterns could be used to adapt the current driver behaviour towards better performance while reducing the task load. Using interaction patterns that escalated according to the drivers actions and the current environmental state, lead to a reduction of temporal demand, effort and frustration. Furthermore less collisions were counted and the overall lateral displacement of the vehicle was reduced. The results were a good mix of encouragement and lessons learned, both for the methodical approach of pattern based human machine interaction, and for the application of AR-based cooperative guidance and control.