根据场景复杂度动态调整感知帧率，省电76%且性能不降。
TAPAS: Throughput-adaptive Perception for Autonomous Systems
- 用强化学习动态分配模型到集群，实时适配不同帧率需求。
- 在KITTI上达93%-100%吞吐达标率，能耗降低76%。
- 适合移动/边缘设备，尤其对能效敏感的自动驾驶系统。
自主系统依赖感知模块在动态环境中导航。实际场景中，感知模块的吞吐量需求会随场景复杂度变化而波动。现有方法假设固定帧率和静态模型-集群映射，导致资源过度或不足配置，或产生不必要的能耗。为此，需具备场景复杂度感知能力以估算合适的帧率目标，并实现动态模型-集群映射，以最小能耗满足吞吐要求。本文提出面向移动/边缘平台的吞吐自适应感知策略TAPAS，基于强化学习（RL）与RRM（奖励推理模型）及GRU（门控循环单元）代理，在异构平台上智能调度感知任务。我们在Jetson Orin NX上对KITTI和未见的nuScenes数据集进行评估。在KITTI测试序列上，TAPAS实现93%-100%吞吐达标率，能耗降低76%；在未见nuScenes数据集上，保持97%吞吐达标率，能耗较最先进方法降低64%，验证其鲁棒性。
原文摘要 · Abstract (English)
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.
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