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<p>Autonomous navigation in developing regions is challenged by heterogeneous traffic, dynamic occlusions, and weak road structure. Existing segmentation models, largely trained on structured Western datasets, struggle to generalize under these conditions. To address this gap, we propose IndiVNet, a semantic segmentation architecture tailored for unstructured Indian driving environments. IndiVNet introduces a progressive dilation encoder (6<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\rightarrow\)</EquationSource> </InlineEquation>16) that captures fine-grained details and broad contextual cues without inducing oversparsity. Evaluated on the India Driving Dataset (IDD), it achieves 69.98% mIoU, outperforming CNN and Transformer baselines, and reaches 73.2% mIoU on CAMVID, demonstrating strong cross-domain generalization. By combining contextual adaptability with real-time efficiency, IndiVNet offers a scalable, region-aware solution for robust autonomous navigation in complex environments.</p>

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IndiVNet A region adaptive semantic image segmentation for autonomous driving in unstructured environments

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月20日

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  • Pritam Chakraborty,
  • Anjan Bandyopadhyay,
  • Siddhartha Bhattacharyya,
  • Jan Platos

摘要

Autonomous navigation in developing regions is challenged by heterogeneous traffic, dynamic occlusions, and weak road structure. Existing segmentation models, largely trained on structured Western datasets, struggle to generalize under these conditions. To address this gap, we propose IndiVNet, a semantic segmentation architecture tailored for unstructured Indian driving environments. IndiVNet introduces a progressive dilation encoder (6 \(\rightarrow\) 16) that captures fine-grained details and broad contextual cues without inducing oversparsity. Evaluated on the India Driving Dataset (IDD), it achieves 69.98% mIoU, outperforming CNN and Transformer baselines, and reaches 73.2% mIoU on CAMVID, demonstrating strong cross-domain generalization. By combining contextual adaptability with real-time efficiency, IndiVNet offers a scalable, region-aware solution for robust autonomous navigation in complex environments.