In this paper we introduce a general estimation methodology for learning a model of human perception and control in a sensorimotor control task based upon a finite set of demonstrations. The model’s structure consists of (i) the agent’s internal representation of how the environment and associated observations evolve as a result of control actions and (ii) the agent’s preferences over observable outcomes. We consider a model’s structure specification consistent with active inference, a theory of human perception and behavior from cognitive science. According to active inference, the agent acts upon the world so as to minimize surprise defined as a measure of the extent to which an agent’s current sensory observations differ from its preferred sensory observations. We propose a bi-level optimization approach to estimation which relies on a structural assumption on prior distributions that parameterize the statistical accuracy of the human agent’s model of the environment. To illustrate the proposed methodology, we present the estimation of a model for car-following behavior based upon a naturalistic dataset. Overall, the results indicate that learning active inference models of human perception and control from data is a promising alternative to closed-box models of driving.


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    Title :

    Learning an Active Inference Model of Driver Perception and Control: Application to Vehicle Car-Following


    Contributors:


    Publication date :

    2025-07-01


    Size :

    4471584 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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