---

name: setup-and-scenarios description: "Install and diagnose OpenCDA, select a supported scenario, compose default-plus-override YAML, and run only when external CARLA or co-simulation prerequisites are ready." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

OpenCDA setup and scenarios

Use this sub-skill when a Researcher needs to prepare OpenCDA 0.1.3, inspect the scenario CLI, choose a benchmark, or diagnose a configuration/prerequisite failure. Start with installation-and-prerequisites.md, then use cli-reference.md, scenario-catalog.md, and configuration.md. Use scripts/check_scenario_cli.py for a static, non-simulation repository check.

Operating boundary

• Run commands from any directory by passing the repository root explicitly; do not assume the current directory is the checkout.
• opencda.py --help and static checks are local and safe. A benchmark run is not a smoke test: it needs a reachable CARLA server, matching CARLA Python API, installed maps, display/GPU resources as applicable, and a compatible scenario configuration.
• CARLA-only scenarios do not require SUMO. Co-simulation scenarios require a SUMO installation, traci, SUMO network/route files, and the co-simulation manager; they are outside the CARLA-only fast path.
• Keep --apply_ml off for the minimal baseline unless the selected scenario explicitly needs perception. The inspected environment did not verify PyTorch or YOLOv5, CARLA server, SUMO, ScenarioRunner, or YOLO runtime.
• Never launch a simulator from the static checker, and do not claim a scenario passed until its external backend and map are actually available.

Procedure

1. Check the source tree and CLI without simulation: python <repo-root>/opencda.py --help and python <skill-root>/scripts/check_scenario_cli.py --repo-root <repo-root>.
2. Select a catalog entry and verify its CARLA version, map, ML, and traffic requirements. Prefer a CARLA-only entry with ML disabled for a dependency diagnosis.
3. Prepare the Python dependencies and CARLA API as documented. Verify python -c "import carla" separately from server connectivity.
4. Start the external CARLA server with the required map assets, then run the exact CLI command. Use synchronous mode; the configuration and Traffic Manager settings must agree.
5. Use the evaluation entry point described in cli-reference.md after the run, and preserve simulator logs separately from the skill.

For predictable failures, consult troubleshooting.md. Docker is a reference deployment, not proof that the host has a working GPU, X11/Vulkan path, CARLA server, or map assets; see container-deployment.md.