Learning An Active Inference Model of Driver Perception and Control: Application to Vehicle Car-Following
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 78 references that are in the map. First posted on arXiv 27 Mar 2023.
Builds on
22 of 78 references.
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Citing papers per year: 2023: 2.
2 papers.
- 2023Resolving uncertainty on the fly: Modeling adaptive driving behavior as active inference10 Nov 2023
- 2023Measuring Surprise in the Wild12 May 2023