Learning-based motion planners, despite recent progress, often suffer from temporal inconsistency. Small perturbations across frames can accumulate into unstable trajectories, degrading comfort and safety in closed-loop driving. Several methods attempt to inject history as a static conditioning signal to stabilize outputs, only to induce the planner to copy historical patterns instead of adapting to environment contexts. To address this limitation, we propose Diffusion Forcing Planner (DFP), a diffusion-based planning framework driven by history-guided control. Specifically, DFP decomposes the full trajectory into history, current and future segments, and assign independent noise levels to each segment. The model jointly denoises the historical and the future segments, enforcing a heterogeneous joint diffusion process. At inference, classifier-free guidance (CFG) is applied to steer future sampling using annealed history in a controllable manner. Closed-loop evaluation and comprehensive ablations on nuPlan show that DFP achieves competitive performance while producing continuous, stable, and controllable motion plans in complex driving scenarios.

翻译：尽管基于学习的运动规划器近期取得了进展，但常受时序不一致性困扰。帧间微小扰动可能累积形成不稳定轨迹，降低闭环驾驶的舒适性与安全性。现有方法尝试将历史信息作为静态条件信号注入以稳定输出，却导致规划器机械复制历史模式而非适应环境上下文。为解决此局限，我们提出扩散强制规划器（DFP），一种由历史引导控制的扩散式规划框架。具体而言，DFP将完整轨迹分解为历史、当前和未来三段，并为每段分配独立噪声水平。模型联合去噪历史段与未来段，实现异质联合扩散过程。在推理阶段，采用无分类器引导（CFG），通过可控方式利用退火历史引导未来采样。在nuPlan上的闭环评估与全面消融实验表明，DFP在保持竞争性能的同时，能在复杂驾驶场景中生成连续、稳定且可控的运动规划。