SparseDrive is an end-to-end autonomous driving framework that processes multi-view camera images to perform unified perception and planning. It simultaneously predicts 3D object bounding boxes with tracking IDs, vectorized HD map elements (dividers, boundaries, pedestrian crossings), agent motion trajectories, and ego vehicle planning trajectories. The architecture employs a ResNet-50 FPN backbone, deformable feature aggregation for multi-view fusion, instance banks for temporal object representation, and specialized heads for each task. Key innovations include hierarchical planning with collision-aware rescore and symmetric sparse representation for detection, mapping, and motion tasks. The system is designed for efficiency, achieving 9.0 FPS on NVIDIA A100 with significantly reduced training time compared to prior art like UniAD.
Triage is a Python framework for end-to-end machine learning workflows focused on temporal prediction problems, particularly in social good domains. It provides modular components for feature generation (architect), model training/evaluation (catwalk), experiment orchestration, model selection (audition), and post-modeling analysis. The system uses PostgreSQL for storing experiment metadata, feature matrices, and model predictions, with SQLAlchemy ORM and Alembic migrations managing the schema. Key entry points include the CLI (`triage experiment`, `triage audition`) and `manage.py` for development tasks. The architecture emphasizes reproducibility through configuration-driven experiments, temporal validation to prevent lookahead bias, and metadata tracking. While powerful, the system has complexity from multiple interdependent components and external service dependencies (PostgreSQL, Redis for RQ).