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arXiv · 2605.30939

IAF-Net: Illumination-Adaptive Fusion for Low-Light Urban Road Segmentation

Bingtao Wang·Daojie Peng·Fulong Ma·Jun Ma·Liang Zhang
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Abstract

Semantic road segmentation is important for autonomous driving, but existing methods suffer severe performance degradation under low-light conditions. Many existing multi-modal fusion methods do not explicitly adapt to illumination-dependent changes in modality reliability, which can propagate degraded RGB features into the fused representation at night. We propose IAF-Net (Illumination-Adaptive Fusion Network), an end-to-end framework with illumination-adaptive fusion for robust road segmentation across different lighting conditions. It dynamically adjusts fusion weights of RGB and geometric features via the core Illumination-Adaptive Fusion (IAF) module, and enhances low-light feature selection with a brightness-modulated attention decoder. We also construct two dedicated datasets: nuScenes Nighttime Road Segmentation (nuScenes-NRS) and CARLA Multi-Weather Road Segmentation (CARLA-MWRS). Experiments on nuScenes-NRS show state-of-the-art overall performance among the compared methods, while CARLA-MWRS further validates robustness across adverse weather conditions. Ablation studies on a 40% training subset further highlight the importance of the IAF module, which provides the largest individual gain of 0.70% in MaxF.

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Bingtao Wang, Daojie Peng, Fulong Ma, Jun Ma, Liang Zhang. 2026-05-29. IAF-Net: Illumination-Adaptive Fusion for Low-Light Urban Road Segmentation. https://arxiv.org/abs/2605.30939

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