  This paper presents a novel approach to Autonomous Vehicle (AV) control
through the application of active inference, a theory derived from neuroscience
that conceptualizes the brain as a predictive machine. Traditional autonomous
driving systems rely heavily on Modular Pipelines, Imitation Learning, or
Reinforcement Learning, each with inherent limitations in adaptability,
generalization, and computational efficiency. Active inference addresses these
challenges by minimizing prediction error (termed "surprise") through a dynamic
model that balances perception and action. Our method integrates active
inference with deep learning to manage lateral control in AVs, enabling them to
perform lane following maneuvers within a simulated urban environment. We
demonstrate that our model, despite its simplicity, effectively learns and
generalizes from limited data without extensive retraining, significantly
reducing computational demands. The proposed approach not only enhances the
adaptability and performance of AVs in dynamic scenarios but also aligns
closely with human-like driving behavior, leveraging a generative model to
predict and adapt to environmental changes. Results from extensive experiments
in the CARLA simulator show promising outcomes, outperforming traditional
methods in terms of adaptability and efficiency, thereby advancing the
potential of active inference in real-world autonomous driving applications.
