UniTeD: Unified Temporal Diffusion for Joint Perception and Planning in Autonomous Driving

Diffusion models have shown strong potential for multi-modalplanning in end-to-end autonomous driving. However, most existing meth-ods confine diffusion to the planning module, conditioning on fixed out-puts from separate discriminative perception networks. This decoupleddesign propagates perception errors to the planner, increasing optimiza-tion difficulty and reducing robustness. To overcome these limitations, wepropose UniTeD, a Unified Temporal Diffusion framework that jointlymodels perception and planning through iterative denoising in a sharedgenerative space. By enabling bidirectional information exchange, theframework facilitates mutual refinement between tasks and improves ro-bustness via noise-conditioned multi-task training. We further extendthis unified diffusion paradigm to a streaming setting by incorporat-ing temporal context. A Temporal Transition Module (TTM) is intro-duced to resolve the noise-level mismatch between historical and currentframes. In addition, we propose an Anchor Refresh Strategy (ARS) toalleviate the training–inference distribution shift commonly observed insparse diffusion-based end-to-end driving frameworks. Without bells andwhistles, UniTeD achieves state-of-the-art performance across multiplebenchmarks, surpassing both recent discriminative end-to-end methodsand diffusion-based planning approaches.