Patchdrivenet

PatchDriveNet is a neural-network-based method (or model family) for image/visual tasks that focuses on processing images as sequences of patches rather than full-resolution grids — conceptually similar to Vision Transformers but optimized for efficiency and locality. It emphasizes patch-level representations, local attention, and lightweight modules to run well on limited compute.

The input image (e.g., 2048x2048) is immediately reduced to a 256x256 "ghost view" via adaptive average pooling. This 256x256 tensor is fed into a lightweight backbone (like MobileNetV3 or EfficientNet-Lite).

Output: A coarse feature map that knows "there is a car" or "there is a tumor," but not where the edges are. patchdrivenet

Three task-specific heads branch from the final patch representations:

Report No: TR-PDN-2026-01
Date: April 12, 2026
Author: AI Research Unit Detecting small boats in a vast ocean

Detecting small boats in a vast ocean. Global context identifies the water-sky boundary; the Patch Drive focuses on whitecaps and wake trails. Result: False positives from wave noise reduced by 60%.

No architecture is perfect. PatchDriveNet struggles with: The next evolution of PatchDriveNet will likely incorporate

The next evolution of PatchDriveNet will likely incorporate event-based cameras (spiking neural drives) or hardware-level support for "crop by index" to eliminate the CPU-GPU synchronization bottleneck of dynamic cropping.

| Configuration | mAP | FPS | Notes | |---------------|-----|-----|-------| | Fixed 16×16 patches | 0.571 | 202 | Poor small object detection | | Global self-attention | 0.619 | 104 | Too slow for real-time | | Without temporal reuse | 0.628 | 98 | Shows reuse hurts accuracy only minimally | | Dynamic patches (full model) | 0.634 | 176 | Best trade-off |

Abstract 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.