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%.

翻译：在偏远环境中部署的自动驾驶车辆通常依赖嵌入式处理器、紧凑型电池和轻量级传感器。这些硬件限制与获取环境鲁棒表征的需求相冲突——后者往往需要执行计算密集型的深度神经网络进行感知。为应对这一挑战，我们提出CADENCE自适应系统，该系统能根据导航需求与环境上下文动态调整可伸缩单目深度估计网络的计算复杂度。通过构建感知精度与驱动需求之间的闭环，CADENCE确保高精度计算仅在任务关键场景下启用。我们在开源测试平台上展开评估（该平台集成了Microsoft AirSim与NVIDIA Jetson Orin Nano）。相较现有静态方法，CADENCE将传感器采集次数、功耗和推理延迟分别降低9.67%、16.1%和74.8%。结果表明，系统总能耗减少75.0%，导航精度提升7.43%。