发表机构
University of Turku（图尔库大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自主系统感知模块吞吐量需求随场景变化的问题，提出基于强化学习的吞吐量自适应感知策略TAPAS，能在异构移动/边缘平台智能分配资源，在KITTI和nuScenes数据集上评估，吞吐量满足率高且节能效果显著。
AI 中文摘要
自主系统依靠感知模块在动态环境中导航。在现实场景中，由于场景复杂性变化，感知模块的吞吐量需求在运行时会有所不同。现有策略存在不足，本文提出一种吞吐量自适应感知策略，用于移动/边缘平台，基于不同的FPS目标实现智能运行时资源分配。利用强化学习、奖励推理模型和门控循环单元代理在异构移动/边缘平台协调感知任务。在Jetson Orin NX上针对KITTI和nuScenes数据集评估，结果表明该策略在吞吐量满足率和节能方面表现出色，证明了其鲁棒性。
英文摘要
Autonomous systems rely on a perception module to navigate through dynamic environments. In real-world scenarios, the perception module's throughput requirements vary at runtime due to changes in scene complexity. However, existing perception strategies assume a fixed FPS and static model-to-cluster mapping, resulting in either over/under provision of throughput requirements or unnecessary energy consumption across diverse scenes. Addressing this challenge requires tightly coupled \textit{scene complexity awareness} to estimate an appropriate FPS target and \textit{dynamic model-to-cluster mapping} to deliver the required throughput at minimum energy. We propose a throughput-adaptive perception strategy for mobile/edge platforms, enabling intelligent runtime resource allocation based on varying FPS targets. We use Reinforcement Learning (RL) with RRM (Reward Reasoning Model) and a GRU (Gated Recurrent Unit) agent to orchestrate perception tasks across heterogeneous mobile/edge platforms. We evaluate TAPAS on Jetson Orin NX across KITTI and unseen nuScenes. On the \textit{KITTI} dataset's test sequences, TAPAS achieves 93-100% throughput met rate while saving energy by 76%. On the unseen \textit{nuScenes} dataset, TAPAS maintains 97% throughput met rate with 64% lower energy compared to \textit{SOTA} approaches, proving its robustness.
Comments15 Pages, 20 Figures, Accepted at ACM/IEEE International Conference on Codesign of Embedded Systems at Embedded Systems Week (ESWEEK-CODES), 2026
