Patchdrivenet May 2026

In the broader field of computer vision , "Patch-based" networks are often developed to make models more robust. Instead of looking at a single global image, the network analyzes small, localized "patches."

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Real-time perception in autonomous driving requires a trade-off between global contextual awareness and computational efficiency. This paper introduces PatchDriveNet, a novel neural network architecture that processes driving scenes via hierarchical patch embedding. Unlike standard convolutional networks that operate on fixed pixel grids or vision transformers that rely on global self-attention, PatchDriveNet divides the Bird’s Eye View (BEV) or front-facing image into dynamic semantic patches. We demonstrate that patch-level feature extraction reduces latency by 40% compared to standard ViT while achieving superior lane detection and obstacle segmentation accuracy on the nuScenes dataset. patchdrivenet