Motion planning in dynamic urban environments requires balancing immediate safety with long-term goals. While diffusion models effectively capture multi-modal decision-making, existing approaches treat trajectories as monolithic entities, overlooking heterogeneous temporal dependencies where near-term plans are constrained by instantaneous dynamics and far-term plans by navigational goals. To address this, we propose Temporally Decoupled Diffusion Model (TDDM), which reformulates trajectory generation via a noise-as-mask paradigm. By partitioning trajectories into segments with independent noise levels, we implicitly treat high noise as information voids and weak noise as contextual cues. This compels the model to reconstruct corrupted near-term states by leveraging internal correlations with better-preserved temporal contexts. Architecturally, we introduce a Temporally Decoupled Adaptive Layer Normalization (TD-AdaLN) to inject segment-specific timesteps. During inference, our Asymmetric Temporal Classifier-Free Guidance utilizes weakly noised far-term priors to guide immediate path generation. Evaluations on the nuPlan benchmark show TDDM approaches or exceeds state-of-the-art baselines, particularly excelling in the challenging Test14-hard subset.

翻译：城市动态环境中的运动规划需要在即时安全与长期目标之间取得平衡。尽管扩散模型能有效捕捉多模态决策，现有方法将轨迹视为整体，忽略了异质性时间依赖关系——近端规划受瞬时动力学约束，远端规划则受导航目标引导。为此，我们提出时序解耦扩散模型（TDDM），该模型通过“噪声即掩码”范式重构轨迹生成过程。通过将轨迹划分为具有独立噪声水平的片段，我们隐含地将高噪声视为信息缺失、弱噪声视为上下文线索，迫使模型利用内部相关性与更完整保留的时序上下文重建受损的近端状态。在架构设计上，我们提出时序解耦自适应层归一化（TD-AdaLN）以注入片段特定时间步。推理阶段，非对称时序无分类器引导利用弱噪声远端正则先验指导即时路径生成。在nuPlan基准上的评估表明，TDDM接近或超越现有最优基线，尤其在高难度Test14-hard子集上表现突出。