SlackDrive: Reclaiming Runtime Slack for Adaptive Driving Inference· SlackDrive：利用运行时松弛资源的自适应驾驶推理
Driving world-action models improve planning by coupling multimodal reasoning with future prediction, but their growing inference cost increasingly conflicts with the real-time latency requirements of vehicle control. Existing acceleration methods reduce tokens, layers, or sampling steps with policies selected prior to deployment, yet leave residual runtime variation largely unexploited after offline profiling and static scheduling on shared onboard compute. We observe that the largest admissible compute budget varies systematically with the residual runtime state, while recent realized latency provides a direct signal of the available compute slack. Motivated by this observation, we propose \textbf{SlackDrive}, a pre-inference compute allocator that reuses realized latency to select the c
利用运行时松弛资源优化驾驶模型推理，提高实时性与灵活性。
- 核心方法
- SlackDrive 在预推理阶段根据已实现的延迟动态分配计算资源，以适应运行时的变化。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 驾驶模型的推理成本与车辆控制的实时需求之间的矛盾日益突出。
- 关键实验
- 未提供
- 主要贡献
- 提出了利用实时计算松弛资源的自适应驾驶推理方法，提高了模型的灵活性和实时性能。
- 意义与局限
- 该方法能够在不影响准确率的前提下，显著降低驾驶模型的推理延迟，提高车载计算资源的利用率。然而，其适用性可能受限于特定的硬件和软件环境。