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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:
    Wei, Ran (Autor:in) / Garcia, Alfredo (Autor:in) / McDonald, Anthony (Autor:in) / Markkula, Gustav (Autor:in) / Engstrom, Johan (Autor:in) / O'Kelly, Matthew (Autor:in)


    Erscheinungsdatum :

    01.07.2025


    Format / Umfang :

    4471584 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Vehicle following model stability control method considering driver perception error

    ZHANG JUNJIE / YU HAIYANG / REN YILONG et al. | Europäisches Patentamt | 2020

    Freier Zugriff

    Deep learning car-following prediction method considering driver fuzzy perception

    LI LINBO / LI RUIJIE / LI YANG et al. | Europäisches Patentamt | 2021

    Freier Zugriff

    Improved adaptive neuro fuzzy inference system car-following behaviour model based on the driver–vehicle delay

    Khodayari, Alireza / Ghaffari, Ali / Kazemi, Reza et al. | IET | 2014

    Freier Zugriff

    Improved adaptive neuro fuzzy inference system car‐following behaviour model based on the driver–vehicle delay

    Khodayari, Alireza / Ghaffari, Ali / Kazemi, Reza et al. | Wiley | 2014

    Freier Zugriff

    Driver intention inference with vehicle onboard sensors

    Berndt, Holger / Dietmayer, Klaus | IEEE | 2009