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データ・研究 · SKILL.md

customization-and-analysis

customization-and-analysis

Extend OpenCDA localization, perception, behavior, and control modules while preserving runtime contracts, and analyze KF/EKF output with safe diagnostics.

Independent GitHub snapshot · Automated classification · Original documentation governs

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name: customization-and-analysis description: "Extend OpenCDA localization, perception, behavior, and control modules while preserving runtime contracts, and analyze KF/EKF output with safe diagnostics." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Customization and Analysis

Use this sub-skill when an OpenCDA module must be subclassed or replaced, or when localization filters and their debug output need to be inspected. Keep custom modules under the package's public opencda/customize/... convention where possible, preserve the contracts in references/extension-contracts.md, and wire the replacement explicitly; localization, perception, and behavior are not discovered from YAML by the stock VehicleManager.

Route the work

  1. Identify one seam: LocalizationManager, PerceptionManager, BehaviorAgent, or the controller object selected by ControlManager.
  2. Read references/extension-contracts.md before changing a signature or an object shape.
  3. For a KF/EKF change or numerical investigation, use filter-and-debugging.md. Prefer a bounded, non-interactive diagnostic over starting a simulator.
  4. If an import, YAML selection, or plotting operation fails, follow references/troubleshooting.md.
  5. Validate with the native KF/EKF and debug-helper tests when available, then run a synthetic seam check that exercises the changed input/output contract. Do not claim live-simulation validation unless a CARLA server was actually available.

Non-negotiable limits

The inspected environment proved Python 3.8, OpenCDA 0.1.3 imports, the CARLA 0.9.12 client import, numerical/plotting dependencies, and core manager imports. It did not prove a live CARLA server, SUMO, ScenarioRunner, torch, or YOLOv5. Sensor spawning, ML perception, and co-simulation therefore remain external backend gates rather than local guarantees. Keep diagnostics headless and do not require those backends for filter-only checks.

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