F2-Planner: Generative Replanning from Unexecuted Future Trajectories

Generative trajectory planners repeatedly refine future trajectories, although only a short prefix of each selected plan is executed before replanning. The unexecuted future offers a structured starting point, but vehicle motion and new observations can make it outdated. We propose F2-Planner, a future-to-future generative replanner that carries the unexecuted portion of the controller-selected trajectory across cycles as a revisable generative state. It first advances the previous plan and aligns the remaining waypoints with the current ego pose. A horizon-wise utility predictor then controls how much of this future is blended with current candidates and how broadly the resulting source is sampled. On Bench2Drive v0.0.4, F2-Planner achieves a mean Driving Score of 96.65 with two neural function evaluations across eight training seeds. It outperforms the matched four-evaluation cycle-local planner (96.01) while reducing planning-head latency by 27.77%. At the same two-evaluation budget and nearly matched planning-head latency, it improves over an execution-aligned warm-start by 0.42 Driving Score points. Separate event-response and corrupted-history tests show that the carried future can be revised when its intent becomes stale. The source update also improves a truncated-diffusion generator within the same planning stack. These results support reusing and revising an unexecuted future to reduce repeated refinement while retaining responsiveness in the evaluated scenarios.