HAD: Combining Hierarchical Diffusion with Metric-Decoupled RL for End-to-End Driving

End-to-end planning has emerged as a dominant paradigmfor autonomous driving, where recent models often adopt a scoring-selection framework to choose trajectories from a large set of candidates,with diffusion-based decoding showing strong promise. However, directlyselecting from the entire candidate space remains difficult to optimize,and Gaussian perturbations used in diffusion often introduce unrealis-tic trajectories that complicate the denoising process. In addition, fortraining these models, reinforcement learning (RL) has shown promise,but existing end-to-end RL approaches typically rely on a single coupledreward without structured signals, limiting optimization effectiveness.To address these challenges, we propose HAD, an end-to-end planningframework with a Hierarchical Diffusion Policy that decomposes planninginto a coarse-to-fine process. To improve trajectory generation, we intro-duce Structure-Preserved Trajectory Expansion, which produces realisticcandidates while maintaining kinematic structure. For policy learning, wedevelop Metric-Decoupled Policy Optimization (MDPO) to enable struc-tured RL optimization across multiple driving objectives. Extensive ex-periments show that HAD achieves new state-of-the-art performance onboth NAVSIM and HUGSIM, outperforming prior arts by a huge margin:+2.3 EPDMS on NAVSIM and +4.9 Route Completion on HUGSIM.