In this paper, we address the challenges of limited interpretability and scalability in traditional trajectory prediction models for autonomous driving decision-making. We present the Dynamic Insertable Intention Interval framework (DI3), which introduces a novel representation of driving intentions by accounting for dynamic interactions with the surrounding environment. Our hierarchical approach integrates intention queries within a motion decoder, enabling the generation of multimodal predictions that closely replicate human driving behavior. Through comprehensive experiments on the highway on-ramp merging scenario using the exiD dataset, we demonstrate that DI3 enhances trajectory prediction accuracy and reduces joint prediction overlap rates compared to the Motion Transformer (MTR) baseline, demonstrating its effectiveness in high-interaction scenarios. Our work lays the foundation for more reliable and interpretable prediction models that is valuable for decision-making in autonomous driving applications.