openpilot is a full-stack autonomous driver assistance system that runs on embedded Linux hardware (comma four/three) and controls 300+ car models. It is a safety-critical, real-time system written primarily in Python and C/C++, with a custom messaging layer (cereal/msgq) and a Cap'n Proto-based schema for all inter-process communication. The architecture is a collection of independent processes (controlsd, plannerd, radard, modeld, locationd, calibrationd, dmonitoringd, etc.) communicating via a pub/sub message queue, managed by a central process manager. The system ingests CAN bus data, camera streams (road, wide-road, driver), IMU/GPS sensor data, and runs neural network models (ONNX/tinygrad) for perception and planning. The codebase demonstrates strong engineering discipline: a comprehensive process replay regression testing framework that replays logged routes through individual processes and compares outputs bit-for-bit; a safety model implemented in the panda firmware (separate submodule) with ISO 26262 guidelines; real-time scheduling and CPU affinity management across processes; and a sophisticated build system using SCons with Cython compilation. The UI is built on raylib (with a Qt-based Cabana tool for CAN analysis), and there's a MetaDrive simulator bridge for development. Key architectural patterns include: a generic car interface abstraction (opendbc) that allows supporting 300+ car models through fingerprinting and parameter configuration; a model-driven control pipeline where neural network outputs (desired curvature, lead vehicle trajectories) feed into lateral controllers (PID, torque-based, curvature-based, angle-based) and longitudinal MPC planners; an EKF-based sensor fusion system (locationd) combining GPS, IMU, and vehicle odometry; and a driver monitoring system using a separate neural network for distraction detection. The system uses a parameter store (params) backed by a filesystem for persistent configuration. The repository includes extensive tooling: replay infrastructure for regression testing, PlotJuggler/Cabana integration for data visualization, automated car porting tools with fuzzy fingerprinting, release automation with stripped builds, and a comprehensive CI/CD pipeline with GitHub Actions. The codebase is actively maintained with daily commits, a structured release process (release-mici/release-tizi branches), and active community contribution through Discord and GitHub issues.
Aerugo is a safety-critical real-time operating system (RTOS) for embedded systems, specifically targeting the ARM Cortex-M7 (ATSAMV71) microcontroller. Developed under an ESA contract for space applications, it provides a non-preemptive, cooperative scheduler based on fine-grained tasklets, along with event-driven and cyclic execution, message queues, boolean conditions, and execution monitoring. The system is designed for static allocation, no dynamic memory, and no_std environments. The repository includes a full hardware abstraction layer (HAL) for the SAMV71, covering SPI, UART, PIO, timers, watchdog, XDMAC, PMC, FPU, and more, built on top of an auto-generated peripheral access crate (PAC) from the vendor SVD. A custom Python test framework (calldwell) enables hardware-in-the-loop testing via RTT, GDB, and SSH, with formal requirements traceability embedded in test code. The architecture is modular: the core RTOS kernel resides in `src/`, platform-specific code in `arch/` (Cortex-M and x86), and reusable utilities in `utils/` (bitfield-enum macro, bounded integers, LSM6DSO IMU driver). The HAL uses typestate patterns to enforce correct driver configuration at compile time. The codebase demonstrates strong Rust embedded engineering practices, including manual vtables for type erasure, critical-section-based mutexes, and static resource management via OnceCell. However, it relies on nightly Rust features (const_mut_refs, proc_macro_tracked_env) and the PAC is auto-generated, introducing maintenance risks. The project is actively developed with a comprehensive test suite and real-world demos (e.g., SPI accelerometer with CCSDS telemetry).