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name: opencda description: "Use OpenCDA for cooperative-driving automation research in CARLA, optional CARLA-SUMO co-simulation, single-CAV and platooning scenarios, configurable sensing/planning/control, V2X, customization, and offline evaluation." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

OpenCDA

OpenCDA 0.1.3 is a Python cooperative-driving automation framework whose runtime centers on a synchronous CARLA world and modular CAV managers. Use this skill to plan or diagnose a scenario, configure modules, extend an algorithm, operate V2X/platooning or CARLA-SUMO, and post-process dumped YAML data.

Choose a route

• Install, choose a benchmark, or author/validate YAML: read setup-and-scenarios.
• Single-CAV sensing, map, planning, control, safety, or API lifecycle: read core-driving-stack.
• V2X, platooning, cooperative merge/stability, SUMO, or ScenarioRunner: read cooperative-simulation.
• Replace a module, preserve an extension contract, or analyze KF/EKF/debug output: read customization-and-analysis.
• Augment dumped trajectories, interpret evaluation output, or make headless plots: read data-evaluation.

For cross-cutting install/import/config/backend failures, read references/troubleshooting.md. Check whether this knowledge matches the intended checkout using references/repo-provenance.md.

Minimal setup and safety gate

The documented baseline is Python 3.7+ with the package requirements, a CARLA Python API matching the server (the source documents 0.9.11 and 0.9.12), and a synchronous CARLA server for actual scenarios. Install the package in an isolated environment, then verify at least:

python -m pip install -r requirements.txt
python -m pip install -e .
python -m pip check
python -c "import opencda; from opencda.version import __version__; print(__version__)"
python opencda.py --help

Do not treat a package import or CLI help check as a live simulation check. Before using a benchmark, separately verify the matching CARLA client/server, requested Town05/Town06 or custom map, port, GPU/display requirements, and synchronous settings. Keep --apply_ml off unless the PyTorch/YOLOv5 stack and model assets are provisioned. Co-simulation additionally needs SUMO, traci, SUMO_HOME, and a consistent .sumocfg/.net.xml/.rou.xml map triplet.

Operating invariants

1. Merge the default YAML with the scenario override; do not silently replace the whole configuration. Keep world.sync_mode true and make fixed_delta_seconds agree with Traffic Manager or SUMO.
2. In a CARLA-only loop, tick the scenario manager, update each CAV, run its controller, apply the returned carla.VehicleControl, and destroy actors and sensors on exit.
3. Preserve module contracts: localization produces a CARLA transform and speed, perception returns categorized object dictionaries, behavior returns target speed/location, and control returns a CARLA vehicle command.
4. Treat CARLA server, GPU/rendering, SUMO, ScenarioRunner, and ML model execution as explicit external gates. The construction verification proved offline package/core imports and safe algorithms, not those services.
5. Do not copy source-checkout paths, private environments, large maps, model weights, or generated review artifacts into a runtime task. Use the bundled references and safe helpers instead.