根据导航需求动态调整深度估计计算量，省电提速还更准。
CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency
- 根据环境与任务自动调节深度网络计算量
- 能耗降低75.0%，推理延迟减少74.8%
- 适合嵌入式自动驾驶系统，尤其远程场景
部署在偏远环境的自动驾驶车辆通常依赖嵌入式处理器、小型电池和轻量化传感器。这些硬件限制与构建环境鲁棒表征的需求相冲突，后者常需运行计算密集型深度神经网络进行感知。为此，我们提出CADENCE，一种自适应系统，能根据导航需求与环境上下文动态调整可瘦身单目深度估计网络的计算复杂度。通过将感知精度与执行需求闭环联动，确保仅在任务关键时启用高精度计算。我们在开源测试平台（集成Microsoft AirSim与NVIDIA Jetson Orin Nano）上进行了评估。相比最先进静态方法，CADENCE分别降低传感器采集次数、功耗和推理延迟9.67%、16.1%和74.8%。结果表明整体能耗减少75.0%，导航精度提升7.43%。
原文摘要 · Abstract (English)
Autonomous vehicles deployed in remote environments typically rely on embedded processors, compact batteries, and lightweight sensors. These hardware limitations conflict with the need to derive robust representations of the environment, which often requires executing computationally intensive deep neural networks for perception. To address this challenge, we present CADENCE, an adaptive system that dynamically scales the computational complexity of a slimmable monocular depth estimation network in response to navigation needs and environmental context. By closing the loop between perception fidelity and actuation requirements, CADENCE ensures high-precision computing is only used when mission-critical. We conduct evaluations on our released open-source testbed that integrates Microsoft AirSim with an NVIDIA Jetson Orin Nano. As compared to a state-of-the-art static approach, CADENCE decreases sensor acquisitions, power consumption, and inference latency by 9.67%, 16.1%, and 74.8%, respectively. The results demonstrate an overall reduction in energy expenditure by 75.0%, along with an increase in navigation accuracy by 7.43%.
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