Predicting the focus of attention and deficits in situation awareness with a modular hierarchical Bayesian driver model

Authors:
Claus Möbus;Mark Eilers;Hilke Garbe
Affiliations:
Learning and Cognitive Systems, Transportation Systems, C.v.O University, OFFIS, Oldenburg, Germany;Learning and Cognitive Systems, Transportation Systems, C.v.O University, OFFIS, Oldenburg, Germany;Learning and Cognitive Systems, Transportation Systems, C.v.O University, OFFIS, Oldenburg, Germany
Venue:
ICDHM'11 Proceedings of the Third international conference on Digital human modeling
Year:
2011

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Abstract

Situation Awareness (SA) is defined as the perception of elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future [1]. Lacking SA or having inadequate SA has been identified as one of the primary factors in accidents attributed to human error [2]. In this paper we present a probabilistic machine-learning-based approach for the real-time prediction of the focus of attention and deficits of SA using a Bayesian driver model as a driving monitor. This Bayesian driving monitor generates expectations conditional on the actions of the driver which are treated as evidence in the Bayesian driver model.