DriveCache：面向驾驶世界模型推理的动作感知缓存
DriveCache: Action-Aware Caching for Driving World Model Inference
AI总结：
针对扩散驾驶生成器吞吐量受限的问题，提出动作感知的DriveCache控制器，利用规划运动与动态规划优化缓存，提升保真度-效率权衡，代码将公开。
AI中文摘要：
驾驶视频生成模型通过预测可控的未来场景，为自动驾驶开发提供模拟、规划评估及离线数据生成的支持。基于扩散模型的驾驶生成器会在去噪步骤中反复评估大型主干网络，这限制了生成吞吐量。现有的扩散加速方法虽降低了该成本，但通用设计忽略了生成前可用的驾驶信号，如自车速度和规划轨迹。针对不同驾驶动作的实验表明，缓存容忍度随自车平移、旋转、去噪进度及连续复用长度变化。我们提出DriveCache，一种无需训练、动作感知的控制器，它利用规划的运动在场景间分配特征复用，并在校准后的响应预算下通过动态规划在去噪步骤间布置复用。当生成偏离校准时，因果漂移检查会刷新特征并重新规划剩余调度。在三种生成器配置下，DriveCache相比被评估的缓存方法提升了整体的保真度-效率权衡。我们的代码将公开提供。
英文摘要：
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.
