A Navigable Terrain Segmentation Network using Gaussian Fourier Attention and Entropy–Aware Fusion for wild autonomous driving

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Accurately identifying navigable areas in wild autonomous driving environments using onboard visual sensors remains a challenging task due to highly complex and dynamic terrain. Existing methods often struggle to simultaneously balance global discrimination and local consistency, leading to insufficient terrain understanding. To address this, we propose the Navigable Terrain Segmentation Network (NTSNet), which integrates two core modules: the Gaussian Fourier Attention Module (GFAM) and the Entropy–Aware Fusion Module (EAFM). The GFAM introduces Gaussian Fourier-driven spatial priors to model long-range dependencies, enabling robust extraction of deep global semantic features and enhancing global discrimination. The EAFM, comprising the Attention Entropy Modulation Unit (AEMU) and Weight Entropy Aggregation Unit (WEAU), leverages Shannon entropy to uncover latent advantageous information among neighboring features and adaptively fuse multi-scale representations, improving local consistency. Quantitative and qualitative experiments on two wild terrain datasets demonstrate that the proposed NTSNet achieves competitive segmentation performance against state-of-the-art methods, providing reliable pixel-level terrain perception for wild autonomous driving systems. The code for NTSNet will be made available at https://github.com/lv881314/NTSNet.