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

2026-06-24

AI Summary

This work addresses the challenges of error propagation, optimization difficulty, and insufficient robustness arising from the decoupling of perception and planning in autonomous driving. To this end, the authors propose UniTeD, a unified temporal diffusion framework that jointly models perception and planning within a shared generative space, enabling bidirectional information exchange through iterative denoising. Key innovations include a Temporal Transition Module (TTM) to mitigate noise-level mismatches, an Anchor Refresh Strategy (ARS) to align training and inference distributions, and a noise-conditioned multitask training mechanism. Evaluated on multiple autonomous driving benchmarks, UniTeD significantly outperforms existing discriminative end-to-end approaches and diffusion-based planning methods, achieving state-of-the-art performance.

Abstract

Diffusion models have shown strong potential for multi-modal planning in end-to-end autonomous driving. However, most existing methods confine diffusion to the planning module, conditioning on fixed outputs from separate discriminative perception networks. This decoupled design propagates perception errors to the planner, increasing optimization difficulty and reducing robustness. To overcome these limitations, we propose UniTeD, a Unified Temporal Diffusion framework that jointly models perception and planning through iterative denoising in a shared generative space. By enabling bidirectional information exchange, the framework facilitates mutual refinement between tasks and improves robustness via noise-conditioned multi-task training. We further extend this unified diffusion paradigm to a streaming setting by incorporating temporal context. A Temporal Transition Module (TTM) is introduced to resolve the noise-level mismatch between historical and current frames. In addition, we propose an Anchor Refresh Strategy (ARS) to alleviate the training-inference distribution shift commonly observed in sparse diffusion-based end-to-end driving frameworks. Without bells and whistles, UniTeD achieves state-of-the-art performance across multiple benchmarks, surpassing both recent discriminative end-to-end methods and diffusion-based planning approaches.

Problem

• autonomous driving
• perception-planning integration
• diffusion models
• error propagation
• end-to-end learning

Innovation

• Unified Temporal Diffusion
• Joint Perception and Planning
• Iterative Denoising
• Temporal Transition Module
• Anchor Refresh Strategy