RegAD: Rethinking Regressive versus Generative Planning in End-to-End Autonomous Driving

End-to-end autonomous driving has recently embraced generative planners to model multimodal future trajectories, yet their iterative sampling process introduces substantial latency, and their empirical gains are often entangled with stronger architectures and anchor priors. This paper revisits whether heavy generative models represent the only solution for autonomous driving and fully exploits the potential of regressive planners. We identify the performance bottlenecks in regressive planners and optimize them accordingly. To this end, we introduce an anchor-refinement framework and a scorer design that incorporates soft scoring labels and a scoring label switching strategy. Finally, we construct three regression variants (RegAD) corresponding to three representative generative planners. Extensive experiments on NAVSIM and nuPlan demonstrate that RegAD matches or surpasses generative planners under the same model complexity while achieving faster training convergence and lower inference latency. Quantitatively, RegAD achieves 90.2 PDMS on NAVSIM with only one-fifth of GoalFlow’s latency, and 91.34 non-reactive score on nuPlan with one-eighth of FlowPlanner’s latency, both with other modules identical except the planning head. These results suggest that the key design of high-performance multi-modal planning is not necessarily a generative algorithm, but reliable multimodal refinement and candidate scoring. Code will be released upon acceptance.