CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency

2026-04-08

AI Summary

This work addresses the challenge of balancing perception accuracy and computational efficiency in remote autonomous driving systems, which are constrained by limited computing resources, power budgets, and sensor capabilities on embedded platforms. The authors propose a context-adaptive monocular depth estimation method that, for the first time, closes the loop between perceptual fidelity and navigation task requirements. Their approach employs a slimmable neural network that dynamically adjusts computational complexity, activating high-fidelity inference only in critical scenarios. Evaluated on an open-source platform built with AirSim and Jetson Orin Nano, the system achieves a 16.1% reduction in power consumption, a 74.8% decrease in inference latency, and a 75.0% drop in overall energy usage compared to static baselines, while simultaneously improving navigation accuracy by 7.43% and reducing sensor data acquisition volume by 9.67%.

Abstract

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%.

Problem

• autonomous vehicles
• monocular depth estimation
• computational efficiency
• embedded systems
• energy consumption

Innovation

• adaptive depth estimation
• slimmable neural networks
• computational efficiency
• autonomous navigation
• context-aware perception