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

• URL: http://arxiv.org/abs/2303.15201v2
• Date: Thu, 01 May 2025 17:28:57 GMT
• Title: Learning An Active Inference Model of Driver Perception and Control: Application to Vehicle Car-Following
• Authors: Ran Wei, Anthony D. McDonald, Alfredo Garcia, Gustav Markkula, Johan Engstrom, Matthew O'Kelly,
• Abstract summary: We introduce a general estimation methodology for learning a model of human perception and control in a sensorimotor control task.<n>We consider a model's structure specification consistent with active inference, a theory of human perception and behavior from cognitive science.
• Score: 9.837204436270811
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 black-box models of driving.

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