用驾驶动作信息优化生成模型缓存，提升推理速度与质量。
DriveCache: Action-Aware Caching for Driving World Model Inference
- 根据车辆速度和轨迹预判动态分配缓存使用
- 在三个模型配置下实现更高精度与更快生成速度
- 无需训练，适合自动驾驶仿真与离线数据生成场景
驾驶视频生成模型通过预测可控未来场景，支持自动驾驶的仿真、规划评估与离线数据生成。基于扩散模型的驾驶生成器在去噪过程中反复调用大型主干网络，限制了生成吞吐量。现有加速方法虽能降低计算开销，但通用设计忽略了生成前可获取的驾驶信号，如自车速度和规划轨迹。实验表明，缓存容忍度随自车平移、旋转、去噪进度及连续复用长度而变化。本文提出DriveCache，一种无需训练的动作感知控制器，利用规划运动分配跨场景复用，并通过动态规划在去噪步骤间分配缓存，受校准响应预算约束。当生成偏离校准时，因果漂移检测会刷新特征并重新规划剩余调度。在三种生成器配置下，DriveCache在保真度-效率权衡上优于对比缓存方法。代码将公开。
原文摘要 · Abstract (English)
Driving video generation models support autonomous-driving development by predicting controllable future scenes for simulation, planning evaluation, and offline data generation. Diffusion-based driving generators repeatedly evaluate large backbones across denoising steps, which limits generation throughput. Existing diffusion acceleration methods reduce this cost, but general-purpose designs omit driving signals available before generation, such as ego speed and planned trajectories. Experiments across driving motions show that cache tolerance varies with ego translation and rotation, denoising progress, and consecutive reuse length. We propose DriveCache, a training-free, action-aware controller that uses planned motion to allocate reuse across scenes and dynamic programming to place it across denoising steps under a calibrated response budget. A causal drift check refreshes features and replans the remaining schedule when generation departs from calibration. Across three generator configurations, DriveCache improves the overall fidelity-efficiency trade-off over evaluated cache methods. Our code will be publicly available.
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