autolane/rfdetr-alpr
Object Detection
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Updated
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23
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.
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