Despite recently rapid progress driven by learning-based methods in autonomous driving, existing models often struggle to produce reliable plans in complex multi-agent scenarios. A key reason is the insufficient utilization of contextual information, particularly the rich semantic and interaction cues embedded in the scene, which limits the model’s ability to anticipate how agents will behave. In this paper, we propose a latent intention-guided structure that enhances the model’s ability to extract and utilize underlying behavioral preferences from the scene context. We introduce a latent variable \(\textbf{z}\) as an intermediate representation of fine-grained future motion tendencies, such as turning direction with caution or aggressively, which semantically guide both trajectory prediction and motion planning. This latent variable is modeled as a Gaussian Mixture Model (GMM), capturing the discrete and continuous nature of future intentions in complex multi-agent scenarios. By integrating \(\textbf{z}\) into a Transformer-based encoder-decoder architecture, our method produces intention-aware predictions that are more aligned with scene semantics and agent interactions. Experiments on the nuPlan benchmark demonstrate that our approach significantly improves prediction accuracy and planning performance, while providing interpretable latent structures that reflect diverse behavioral preferences.