用扩散模型统一自动驾驶感知、预测与规划，简化系统结构。
DiffAD: A Unified Diffusion Modeling Approach for Autonomous Driving
- 将自动驾驶建模为条件图像生成任务，统一多任务处理
- 在Carla上实现新最高成功率与驾驶得分
- 适合关注端到端系统简化与协同优化的研究者
端到端自动驾驶（E2E-AD）正成为实现完全自动化的有前景方法。然而，现有E2E-AD系统通常采用传统多任务框架，通过独立的任务专用头部处理感知、预测和规划任务。尽管以全可微方式训练，仍存在任务协调问题，系统复杂度高。本文提出DiffAD，一种新型扩散概率模型，将自动驾驶重新定义为条件图像生成任务。通过将异构目标栅格化至统一鸟瞰图（BEV），并建模其潜在分布，DiffAD在单一框架中统一多种驾驶目标，联合优化所有驾驶任务，显著降低系统复杂度并增强任务协调性。反向过程迭代精炼生成的BEV图像，带来更鲁棒、真实的驾驶行为。在Carla中的闭环评估表明，该方法取得新的最先进成功率与驾驶评分。
原文摘要 · Abstract (English)
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.
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