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Behaviour adaptation using interaction patterns with augmented reality elements

: Baltzer, M.C.A.; Lassen, C.; Lopez, D.; Flemisch, F.


Schmorrow, D.D.:
Augmented cognition. 12th international conference, AC 2018. Proceedings. Pt.1: Intelligent technologies : Held as part of HCI International 2018, Las Vegas, NV, USA, July 15-20, 2018
Cham: Springer International Publishing, 2018 (Lecture Notes in Computer Science 10915)
ISBN: 978-3-319-91469-5 (Print)
ISBN: 978-3-319-91470-1 (Online)
ISBN: 3-319-91469-3
International Conference on Augmented Cognition (AC) <12, 2018, Las Vegas/Nev.>
International Conference on Human-Computer Interaction (HCI International) <20, 2018, Las Vegas/Nev.>
Conference Paper
Fraunhofer FKIE ()

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.