United States Department of Transportation
i
An Active Inference Model of Car Following: Advantages and Applications
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2023-03-28
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[PDF-1.11 MB]
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Abstract:Driver process models play a central role in the testing, verification, and development of automated and autonomous vehicle technologies. Prior models developed from control theory and physics-based rules are limited in automated vehicle applications due to their restricted behavioral repertoire. Data-driven machine learning models are more capable than rule-based models but are limited by the need for large training datasets and their lack of interpretability, i.e., an understandable link between input data and output behaviors.
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Content Notes:This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license https://creativecommons.org/licenses/by/4.0/. Please cite this article as: Wei R., McDonald A.D., Garcia, A., Markkula G., Engstrom J. and O’Kelly M., (2023) An active inference model of car following: Advantages and applications, doi: 10.48550/arXiv.2303.15201
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