EASD: Exposure Aware Single-Step Diffusion Framework for Monocular Depth Estimation in Autonomous Vehicles

Monocular depth estimation (MDE) is a cornerstone of computer vision and is applied to diverse practical areas such as autonomous vehicles, robotics, etc., yet even the latest methods suffer substantial errors in high-dynamic-range (HDR) scenes where over- or under-exposure erases critical texture. To address this challenge in real-world autonomous driving scenarios, we propose the Exposure-Aware Single-Step Diffusion Framework for Monocular Depth Estimation (EASD). EASD leverages a pre-trained Stable Diffusion variational auto-encoder, freezing its encoder to extract exposure-robust latent RGB and depth representations. A single-step diffusion process then predicts the clean depth latent vector, eliminating iterative error accumulation and enabling real-time inference suitable for autonomous vehicle perception pipelines. To further enhance robustness under extreme lighting conditions, EASD introduces an Exposure-Aware Feature Fusion (EAF) module—an attention-based pyramid that dynamically modulates multi-scale features according to global brightness statistics. This mechanism suppresses bias in saturated regions while restoring detail in under-exposed areas. Furthermore, an Exposure-Balanced Loss (EBL) jointly optimises global depth accuracy, local gradient coherence and reliability in exposure-extreme regions—key metrics for safety-critical perception tasks such as obstacle detection and path planning. Experimental results on NYU-v2, KITTI, and related benchmarks demonstrate that EASD reduces absolute relative error by an average of 20% under extreme illumination, using only 60,000 labelled images. The framework achieves real-time performance (<50 ms per frame) and strikes a superior balance between accuracy, computational efficiency, and data efficiency, offering a promising solution for robust monocular depth estimation in challenging automotive lighting conditions such as tunnel transitions, night driving and sun glare.