An active inference model of car following: Advantages and applications

• URL: http://arxiv.org/abs/2303.15201v1
• Date: Mon, 27 Mar 2023 13:39:26 GMT
• Title: An active inference model of car following: Advantages and applications
• Authors: Ran Wei, Anthony D. McDonald, Alfredo Garcia, Gustav Markkula, Johan Engstrom, and Matthew O'Kelly
• Abstract summary: Driver process models play a central role in the testing, verification, and development of automated and autonomous vehicle technologies. 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. We propose a novel car following modeling approach using active inference, which has comparable behavioral flexibility to data-driven models while maintaining interpretability.
• Score: 6.905724739762358
• License: http://creativecommons.org/licenses/by/4.0/
• 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. We propose a novel car following modeling approach using active inference, which has comparable behavioral flexibility to data-driven models while maintaining interpretability. We assessed the proposed model, the Active Inference Driving Agent (AIDA), through a benchmark analysis against the rule-based Intelligent Driver Model, and two neural network Behavior Cloning models. The models were trained and tested on a real-world driving dataset using a consistent process. The testing results showed that the AIDA predicted driving controls significantly better than the rule-based Intelligent Driver Model and had similar accuracy to the data-driven neural network models in three out of four evaluations. Subsequent interpretability analyses illustrated that the AIDA's learned distributions were consistent with driver behavior theory and that visualizations of the distributions could be used to directly comprehend the model's decision making process and correct model errors attributable to limited training data. The results indicate that the AIDA is a promising alternative to black-box data-driven models and suggest a need for further research focused on modeling driving style and model training with more diverse datasets.

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