sunnypilot is a community fork of comma.ai's openpilot, an open-source driver assistance system (ADAS) designed for real-time operation on dedicated hardware (comma three/4, panda). The codebase implements the full autonomy stack: camera and radar perception, path planning, longitudinal/lateral control, driver monitoring, and localization via Kalman filters. It runs as a set of daemon processes orchestrated by a process manager, communicating through a shared-memory pub/sub messaging layer (msgq) with Cap'n Proto serialization. The system includes a full-featured Raylib-based UI with on-road HUD, settings panels, and an installer, plus cloud connectivity through Athena (remote commands, registration) and the fork-specific Sunnylink (settings backup/restore, telemetry). Beyond the core openpilot functionality, sunnypilot adds numerous extended features: Modular Assistive Driving System (MADS) for configurable steering modes, neural network lateral control (NNLC), map-based speed limit assist using OpenStreetMap data (mapd), intelligent cruise button management, dynamic experimental control, and a model manager that fetches and caches driving model bundles from a remote manifest. The fork maintains its own cereal schema extensions and custom UI layouts for both touch (mici) and big-screen (tizi) hardware variants. The repository includes extensive testing infrastructure: unit tests for most modules, process replay tests that compare outputs against reference logs, longitudinal/lateral maneuver simulations, and CAN bus analysis tools (cabana, replay). It also provides simulation bridges (MetaDrive, joystick) for closed-loop testing. The build system uses SCons for C++ components and uv for Python dependencies, with CI pipelines for model building, release drafting, and route uploads.
GrowSP is an unsupervised semantic segmentation framework for 3D point clouds that learns from growing superpoints. The system processes raw point cloud data through preparation pipelines, generates initial superpoints using voxelization and region growing, then trains a Feature Pyramid Network (Res16FPN18) to learn discriminative features. During training, superpoints are progressively merged (growing superpoints strategy) to create hierarchical representations, with pseudo-labels generated via clustering of superpoint features. The framework includes specialized dataset classes for S3DIS, ScanNet, and SemanticKITTI, evaluation scripts using Hungarian matching for unsupervised performance measurement, and utility libraries for 3D augmentations and PLY file handling. The architecture follows a modular design with clear separation between data preparation, dataset handling, model definitions, and training/evaluation logic.