  This paper presents a novel approach to improving autonomous vehicle control
in environments lacking clear road markings by integrating a diffusion-based
motion predictor within an Active Inference Framework (AIF). Using a simulated
parking lot environment as a parallel to unmarked roads, we develop and test
our model to predict and guide vehicle movements effectively. The
diffusion-based motion predictor forecasts vehicle actions by leveraging
probabilistic dynamics, while AIF aids in decision-making under uncertainty.
Unlike traditional methods such as Model Predictive Control (MPC) and
Reinforcement Learning (RL), our approach reduces computational demands and
requires less extensive training, enhancing navigation safety and efficiency.
Our results demonstrate the model's capability to navigate complex scenarios,
marking significant progress in autonomous driving technology.
