Learning An Active Inference Model of Driver Perception and Control: Application to Vehicle Car-Following
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866915268484136960 |
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| author | Wei, Ran McDonald, Anthony D. Garcia, Alfredo Markkula, Gustav Engstrom, Johan O'Kelly, Matthew |
| author_facet | Wei, Ran McDonald, Anthony D. Garcia, Alfredo Markkula, Gustav Engstrom, Johan O'Kelly, Matthew |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_15201 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning An Active Inference Model of Driver Perception and Control: Application to Vehicle Car-Following Wei, Ran McDonald, Anthony D. Garcia, Alfredo Markkula, Gustav Engstrom, Johan O'Kelly, Matthew Machine Learning Artificial Intelligence Robotics 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. |
| title | Learning An Active Inference Model of Driver Perception and Control: Application to Vehicle Car-Following |
| topic | Machine Learning Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2303.15201 |