  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.
