Enhancing Sustainable Intelligent Transportation Systems Through Lightweight Monocular Depth Estimation Based on Volume Density

Abstract

Depth estimation is a critical enabling technology for sustainable intelligent transportation systems (ITSs), as it supports essential functions such as obstacle detection, navigation, and traffic management. However, existing Neural Radiance Field (NeRF)-based monocular depth estimation methods often suffer from high computational costs and poor performance in occluded regions, limiting their applicability in real-world, resource-constrained environments. To address these challenges, this paper proposes a lightweight monocular depth estimation framework that integrates a novel capacity redistribution strategy and an adaptive occlusion-aware training mechanism. By shifting computational load from resource-intensive multi-layer perceptrons (MLPs) to efficient separable convolutional encoder–decoder networks, our method significantly reduces memory usage to 234 MB while maintaining competitive accuracy. Furthermore, a divide-and-conquer training strategy explicitly handles occluded regions, improving reconstruction quality in complex urban scenarios. Experimental evaluations on the KITTI and V2X-Sim datasets demonstrate that our approach not only achieves superior depth estimation performance but also supports real-time operation on edge devices. This work contributes to the sustainable development of ITS by offering a practical, efficient, and scalable solution for environmental perception, with potential benefits for energy efficiency, system affordability, and large-scale deployment.