DiffAD: A Unified Diffusion Modeling Approach for Autonomous Driving

• URL: http://arxiv.org/abs/2503.12170v1
• Date: Sat, 15 Mar 2025 15:23:35 GMT
• Title: DiffAD: A Unified Diffusion Modeling Approach for Autonomous Driving
• Authors: Tao Wang, Cong Zhang, Xingguang Qu, Kun Li, Weiwei Liu, Chang Huang,
• Abstract summary: We introduce DiffAD, a novel diffusion probabilistic model that redefines autonomous driving as a conditional image generation task.<n>Byizing heterogeneous targets onto a unified bird's-eye view (BEV) and modeling their latent distribution, DiffAD unifies various driving objectives.<n>The reverse process iteratively refines the generated BEV image, resulting in more robust and realistic driving behaviors.
• Score: 17.939192289319056
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: End-to-end autonomous driving (E2E-AD) has rapidly emerged as a promising approach toward achieving full autonomy. However, existing E2E-AD systems typically adopt a traditional multi-task framework, addressing perception, prediction, and planning tasks through separate task-specific heads. Despite being trained in a fully differentiable manner, they still encounter issues with task coordination, and the system complexity remains high. In this work, we introduce DiffAD, a novel diffusion probabilistic model that redefines autonomous driving as a conditional image generation task. By rasterizing heterogeneous targets onto a unified bird's-eye view (BEV) and modeling their latent distribution, DiffAD unifies various driving objectives and jointly optimizes all driving tasks in a single framework, significantly reducing system complexity and harmonizing task coordination. The reverse process iteratively refines the generated BEV image, resulting in more robust and realistic driving behaviors. Closed-loop evaluations in Carla demonstrate the superiority of the proposed method, achieving a new state-of-the-art Success Rate and Driving Score. The code will be made publicly available.

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