Autonomous vehicles operating in remote areas face significant computational constraints due to limited processing power, battery life, and sensor capabilities. To mitigate this, researchers have developed CADENCE, a context-adaptive depth estimation system that dynamically adjusts its computational complexity based on the environment. By doing so, CADENCE achieves a balance between perception accuracy and computational efficiency, enabling autonomous vehicles to navigate effectively while minimizing power consumption. The system's adaptability is crucial in addressing the trade-off between robust environmental representation and hardware limitations. This innovation has significant implications for the development of autonomous systems, particularly in resource-constrained environments1. The ability to efficiently estimate depth and navigate through complex environments can substantially enhance the reliability and safety of autonomous vehicles, making this technological advancement a critical consideration for practitioners in the field of autonomous systems.