---

name: core-driving-stack description: "Use the OpenCDA core driving stack knowledge for single-vehicle lifecycle, sensing, geometry, map rasterization, route and behavior planning, PID control, and safety integration." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Core driving stack

Use this sub-skill for a single CAV built from OpenCDA 0.1.3 core modules. It is a routing file: load the references below rather than inferring contracts from memory.

Choose the relevant reference

• Construction and signatures: api-reference.md
• GNSS/IMU/Kalman, sensor data, and matrix geometry: sensing-and-geometry.md
• Map, route/behavior/local planning, PID, and safety: planning-and-control.md
• Import/config/API failures and backend boundaries: troubleshooting.md

Operating workflow

1. Prepare a valid merged scenario configuration. The world block must provide a synchronous CARLA world and fixed_delta_seconds; the single-vehicle config must provide sensing, map_manager, behavior, controller, v2x, and safety_manager blocks. Preserve the units in the references.
2. Create CavWorld(apply_ml=False) unless the ML detector is intentionally available. Construct ScenarioManager with scenario_params, the chosen apply_ml, an explicit CARLA version, and either town or xodr_path; pass the same cav_world.
3. Call create_vehicle_manager(application=['single']). Set the returned manager's destination with CARLA Location objects before entering the loop.
4. On every synchronous tick, call scenario_manager.tick(), then VehicleManager.update_info(), then VehicleManager.run_step(), and apply the returned carla.VehicleControl to the managed vehicle. The update order is localization → perception → map → safety → V2X → behavior → controller.
5. Tear down sensors and actors with the vehicle manager, then restore the world with ScenarioManager.close(). Use CavWorld.destroy() when its registered managers are no longer needed; do not assume the manager's tick() advances CavWorld.global_clock.

Hard boundaries

• The inspected environment passed imports for OpenCDA core dependencies, the CARLA 0.9.12 client, OmegaConf, NumPy/SciPy/matplotlib/networkx/OpenCV/Open3D/Shapely, and core managers. It did not verify a running CARLA server, SUMO, ScenarioRunner, torch, or YOLOv5. Treat live simulation, co-simulation, scenario-runner integration, and active ML perception as conditional—not verified behavior.
• apply_ml=True dynamically loads the ML manager and active perception requires a loaded detector. Keep activate: false for server-ground-truth perception unless the ML runtime and model are actually provisioned.
• The native KF and sensor-transformation tests are pure/mock-oriented candidates. They do not prove sensor spawning, traffic, map topology, controller application, or a live server.
• This sub-skill covers the core single-vehicle path. Platooning, V2X protocol behavior, SUMO bridge details, and data-dump production are only mentioned where they constrain single-vehicle setup.