|
|
--- |
|
|
title: README |
|
|
emoji: ⚡ |
|
|
colorFrom: yellow |
|
|
colorTo: red |
|
|
sdk: static |
|
|
pinned: false |
|
|
--- |
|
|
|
|
|
At Autolane, we’re on a mission to build the AI-driven orchestration layer for autonomous last‑mile delivery. Our R&D team blends state‑of‑the‑art computer vision (YOLOv8, Segment Anything, Detectron2) with geospatial mapping and reinforcement‑learning planners to: |
|
|
|
|
|
**Map & Understand real‑world environments:** from extracting parking stalls and pickup zones in satellite or drone imagery to dynamically segmenting curbside lanes in live video feeds. |
|
|
|
|
|
**Identify & Authenticate vehicles on the move:** developing open‑source ALPR pipelines that robustly read license plates under varied lighting and occlusion conditions. |
|
|
|
|
|
**Optimize & Dispatch with intelligence:** experimenting with graph neural networks and multi‑agent RL for rapid route selection, load balancing across fleets, and on‑the‑fly re‑routing when conditions change. |
|
|
|
|
|
Follow our profile to explore our latest CV models, scheduling agents, and research into federated learning across distributed vehicle fleets. Let’s reimagine logistics with AI, one delivery at a time. |
|
|
|
