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

ArXi:2604.03581v1 Announce Type: cross End-to-end planning has emerged as a dominant paradigm for 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, directly selecting from the entire candidate space remains difficult to optimize, and Gaussian perturbations used in diffusion often