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Predicting the Focus of Attention and Deficits in Situation Awareness with a Modular Hierarchical Bayesian Driver Model

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 6777)

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.

Keywords

  • Focus of attention
  • deficits in situation awareness
  • Bayesian autonomous driver model
  • Bayesian driving monitor
  • modular hierarchical Bayesian driver model
  • learning of action-relevant percepts

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Möbus, C., Eilers, M., Garbe, H. (2011). Predicting the Focus of Attention and Deficits in Situation Awareness with a Modular Hierarchical Bayesian Driver Model. In: Duffy, V.G. (eds) Digital Human Modeling. ICDHM 2011. Lecture Notes in Computer Science, vol 6777. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21799-9_54

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  • DOI: https://doi.org/10.1007/978-3-642-21799-9_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21798-2

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