autolane/rfdetr-alpr
Object Detection
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Updated
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19
AI & ML interests
• Computer Vision & Mapping: Extracting lanes, stalls & pickup zones from satellite/drone imagery and live video feeds. • Semantic Segmentation: Using models like Segment Anything and Detectron2 to delineate curbside environments in real time. • Automated License Plate Recognition (ALPR): Building robust, open‑source pipelines for vehicle identification under diverse conditions. • Graph Neural Networks: Modeling the delivery network as a graph to optimize load balancing and dynamic rerouting. • Reinforcement Learning for Dispatch: Multi‑agent RL to learn efficient task allocation, batching, and on‑the‑fly re‑routing strategies. • Geospatial Data Fusion: Integrating GPS, GIS, and imagery data to create accurate, up‑to‑date environmental maps. • Federated Learning Across Fleets: Enabling decentralized model training on vehicles for privacy‑preserving improvements. • Predictive Maintenance & Anomaly Detection: Monitoring vehicle telemetry to forecast failures and maintain fleet health proactively. • Demand Forecasting & Capacity Planning: Using time‑series forecasting to predict order volumes and optimize fleet sizing. • Edge Inference & Model Compression: Deploying lightweight, accelerated models on‑vehicle for low‑latency decisioning. • Natural Language Order Parsing: NLP pipelines to extract order details from confirmation emails and messages. • Simulation & Digital Twins: Building realistic simulators for training and validating AI behaviors before real‑world rollout. • Safety, Compliance & Explainability: Researching model interpretability and audit‑ready workflows to ensure transparent, safe operations.
Recent Activity
Organization Card
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.
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