The Human Centered Design (HCD) of Partial Autonomous Driver Assistance Systems (PADAS) requires Digital Human Models (DHMs) of human control strategies for traffic scenario simulations. The authors present a probabilistic model architecture for generating descriptive models of human driver behaviour: Bayesian Autonomous Driver (BAD) models. They implement the sensory-motor system of human drivers in a psychological motivated mixture-of-experts (= mixture-of-schema) architecture with autonomous and goal-based attention allocation processes. Under the assumption of stationary behavioural processes models are specified across at least two time slices. Learning data are time series of relevant variables: percepts, goals, and actions. We can represent individual or groups of human and artificial agents. Models propagate information in various directions. When working top-down, goals emitted by a cognitive layer select a corresponding expert (schema), which propagates actions, relevance of areas of interest (AoIs) and perceptions. When working bottom-up, percepts trigger AoIs, actions, experts and goals. When the task or goal is defined and the model has certain percepts evidence can be propagated simultaneously top-down and bottom-up and the appropriate expert (schema) and its behaviour can be activated. Thus, the model can be easily extended to implement a modified version of the SEEV visual scanning or attention allocation model of Horrey, Wickens, and Consalus. In contrast to other authors the model can predict the probability of attending a certain AoI on the basis of single, mixed, and even incomplete evidence (goal priorities, percepts, effort to switch between AoIs). In this paper we present the architecture and a proof of concept with plausible but artificial data.


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    Title :

    Mixture of behaviors in a Bayesian autonomous driver model


    Additional title:

    Verhaltensmischung in einem Bayesschen Fahrmodell


    Contributors:


    Publication date :

    2009


    Size :

    6 Seiten, 7 Bilder, 11 Quellen




    Type of media :

    Conference paper


    Type of material :

    Storage medium


    Language :

    English