DriveCache: Action-Aware Caching for Driving World Model Inference

2026-08-17

AI Summary

This study addresses the inference bottleneck in driving video generation models caused by redundant computations during diffusion. We propose DriveCache, a training-free, action-aware cache controller that pioneers the integration of driving signals into cache scheduling. By optimizing denoising steps through dynamic programming and employing causal drift detection for adaptive feature reuse and correction, DriveCache significantly enhances both inference efficiency and generation fidelity within responsive computational budgets. Experimental evaluations across three generator configurations demonstrate that DriveCache achieves superior efficiency-quality trade-offs compared to existing caching methods. Consequently, this work establishes a novel paradigm for the efficient deployment of world models in autonomous driving applications, effectively balancing computational constraints with high-fidelity video synthesis without requiring additional model retraining.

Abstract

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.

Problem

• Driving World Model
• Diffusion Acceleration
• Inference Throughput
• Action-Aware Caching
• Fidelity-Efficiency Trade-off

Innovation

• Action-Aware Caching
• Training-Free Acceleration
• Dynamic Programming
• Causal Drift Check
• Driving World Model