Temporally Decoupled Diffusion Planning for Autonomous Driving

TL;DR

This paper introduces TDDM, a novel diffusion-based motion planning method for autonomous driving that decouples trajectory segments temporally, improving decision-making in dynamic urban environments.

Contribution

The paper proposes a temporally decoupled diffusion model with a new noise-as-mask paradigm and a segment-specific normalization layer, enhancing trajectory generation for autonomous driving.

Findings

TDDM approaches or exceeds state-of-the-art baselines.

Significant improvement on the challenging Test14-hard subset.

Effective in capturing multi-modal decision-making in urban scenarios.

Abstract

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.