提出分时扩散模型，让自动驾驶更懂远近规划差异。
Temporally Decoupled Diffusion Planning for Autonomous Driving
- 将轨迹分段处理，不同时间段用不同噪声水平建模
- 在nuPlan测试集上表现优于或接近最优基线，尤其在困难场景中
- 适合研究自动驾驶决策与长短期规划的学者
动态城市环境中运动规划需兼顾即时安全与长期目标。现有扩散模型将轨迹视为整体，忽略了近程受实时动态约束、远程受导航目标影响的异质性时间依赖。为此，本文提出分时解耦扩散模型（TDDM），通过噪声作为掩码的范式重构轨迹生成。将轨迹划分为具有独立噪声水平的段落，高噪声表示信息缺失，低噪声作为上下文线索，促使模型利用良好保留的时间上下文重建受损的近程状态。架构上引入分时解耦自适应层归一化（TD-AdaLN）注入段级时间步信息。推理时采用非对称时间无分类引导，利用弱噪声远期先验指导即时路径生成。在nuPlan基准测试中，TDDM达到或超越当前最优基线，尤其在挑战性的Test14-hard子集表现突出。
原文摘要 · Abstract (English)
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.
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