Understanding Driver Intention: Intention-Driven Hierarchical Planning for Robust End-to-End Autonomous Driving

Recent works in imitation-learning-based planning have demonstrated promising scalability in end-to-end autonomous driving, yet they either suffer from mode collapse or directional deviation, failing to generate interpretable multi-modal trajectories that accurately reflect human driving intentions. These issues severely constrain model performance and robustness in complex interactive scenarios, resulting in significant performance degradation in closed-loop evaluations. To address these limitations, we propose Intention Planner, the first intention-driven hierarchical planning framework inspired by the cognitive architecture of human driving behavior. Specifically, our method explicitly models hierarchical driving intention representations from long-term strategic intention to automatic control. With a sophisticatedly designed architecture, these interpretable intentions are introduced as structured conditions to instruct a diffusion-based planner. Our framework also incorporates classifier-free guidance for multi-level behavior generation, which dynamically fuse conscious and unconscious planning systems during inference, enabling the generation of intention-aligned and kinematically feasible trajectories in challenging interactive scenario. Extensive experiments on NAVSIM benchmark demonstrate that Intention Planner outperforms previous end-to-end planners, achieving state-of-the-art performance while maintaining superior robustness in long-tail scenarios.