IndiVNet：非構造化環境における自動運転のための領域適応型セマンティック画像セグメンテーション

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→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.