Learning action-conditioned driving dynamics from raw pixels is challenging due to high dimensionality and weak temporal cues. This work combines Latent Action Pretraining (LAPA) with a conditional Diffusion Model to learn discrete latent actions and predict their future evolution. The framework captures multi-modal driving behaviors in latent space, enabling interpretable and data-efficient policy learning for autonomous driving.